{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/Alex2/Desktop/Documents/GW Fall 2016/Russia Public Opinion Paper/TNPN/Fisher_FPA_TrickleDown_Log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}24 Sep 2019, 21:31:45

{com}. do "/Users/Alex2/Desktop/Documents/GW Fall 2016/Russia Public Opinion Paper/TNPN/Fisher_FP_Replication_9242019.do"
{txt}
{com}. *******************
. *Main Text Figures*
. *******************
. 
. use "/Users/Alex2/Desktop/Documents/GW Fall 2016/Russia Public Opinion Paper/TNPN/TrickleDown_DataSet_Oct2018.dta", clear
{txt}
{com}. keep PutinBi SupportRussiaBi SupportUSBi SupportNatoBi SupportChinaBi  ///
> pr1 ccode year female age agesq unedu blame3 arm_ukraine sanctions c1 country weight
{txt}
{com}. 
. ***********************************
. *Confidence in Putin with Controls*
. ***********************************
. 
. *United Kingdom
. logit PutinBi i.pr1##i.year female age agesq i.unedu  if ccode ==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2434.9586}  
Iteration 1:{space 3}log likelihood = {res:-2374.5405}  
Iteration 2:{space 3}log likelihood = {res:-2372.7909}  
Iteration 3:{space 3}log likelihood = {res:  -2372.79}  
Iteration 4:{space 3}log likelihood = {res:  -2372.79}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,944
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    124.34
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}  -2372.79{txt}{col 49}Pseudo R2{col 67}= {res}    0.0255

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2098011{col 31}{space 2} .4008922{col 42}{space 1}   -0.52{col 51}{space 3}0.601{col 59}{space 4}-.9955354{col 72}{space 3} .5759333
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.0971277{col 31}{space 2} .1227587{col 42}{space 1}   -0.79{col 51}{space 3}0.429{col 59}{space 4}-.3377303{col 72}{space 3} .1434749
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.6806307{col 31}{space 2} .1359025{col 42}{space 1}   -5.01{col 51}{space 3}0.000{col 59}{space 4}-.9469946{col 72}{space 3}-.4142668
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} -.178709{col 31}{space 2}  .113303{col 42}{space 1}   -1.58{col 51}{space 3}0.115{col 59}{space 4}-.4007788{col 72}{space 3} .0433607
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.2631959{col 31}{space 2}  .120347{col 42}{space 1}   -2.19{col 51}{space 3}0.029{col 59}{space 4}-.4990718{col 72}{space 3}-.0273201
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2}  .784164{col 31}{space 2} .4713797{col 42}{space 1}    1.66{col 51}{space 3}0.096{col 59}{space 4}-.1397233{col 72}{space 3} 1.708051
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .5737192{col 31}{space 2} .5059506{col 42}{space 1}    1.13{col 51}{space 3}0.257{col 59}{space 4}-.4179258{col 72}{space 3} 1.565364
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .7916332{col 31}{space 2} .4440965{col 42}{space 1}    1.78{col 51}{space 3}0.075{col 59}{space 4}-.0787799{col 72}{space 3} 1.662046
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} 1.399278{col 31}{space 2} .5627975{col 42}{space 1}    2.49{col 51}{space 3}0.013{col 59}{space 4} .2962147{col 72}{space 3}  2.50234
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.4251279{col 31}{space 2} .0739813{col 42}{space 1}   -5.75{col 51}{space 3}0.000{col 59}{space 4}-.5701285{col 72}{space 3}-.2801273
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0397933{col 31}{space 2} .0105572{col 42}{space 1}   -3.77{col 51}{space 3}0.000{col 59}{space 4} -.060485{col 72}{space 3}-.0191017
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0002935{col 31}{space 2} .0000993{col 42}{space 1}    2.96{col 51}{space 3}0.003{col 59}{space 4}  .000099{col 72}{space 3}  .000488
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.2077077{col 31}{space 2} .0779683{col 42}{space 1}   -2.66{col 51}{space 3}0.008{col 59}{space 4}-.3605228{col 72}{space 3}-.0548926
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .2512954{col 31}{space 2} .2790165{col 42}{space 1}    0.90{col 51}{space 3}0.368{col 59}{space 4}-.2955668{col 72}{space 3} .7981577
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins pr1, at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     4,944
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}_at#pr1 {c |}
{space 4}1#Other  {c |}{col 14}{res}{space 2} .2233581{col 26}{space 2} .0143958{col 37}{space 1}   15.52{col 46}{space 3}0.000{col 54}{space 4} .1951428{col 67}{space 3} .2515734
{txt}{space 6}1#AEP  {c |}{col 14}{res}{space 2} .1895803{col 26}{space 2} .0593664{col 37}{space 1}    3.19{col 46}{space 3}0.001{col 54}{space 4} .0732243{col 67}{space 3} .3059364
{txt}{space 4}2#Other  {c |}{col 14}{res}{space 2} .2072182{col 26}{space 2} .0147234{col 37}{space 1}   14.07{col 46}{space 3}0.000{col 54}{space 4} .1783608{col 67}{space 3} .2360755
{txt}{space 6}2#AEP  {c |}{col 14}{res}{space 2} .3148334{col 26}{space 2} .0490524{col 37}{space 1}    6.42{col 46}{space 3}0.000{col 54}{space 4} .2186924{col 67}{space 3} .4109743
{txt}{space 4}3#Other  {c |}{col 14}{res}{space 2} .1281385{col 26}{space 2} .0117168{col 37}{space 1}   10.94{col 46}{space 3}0.000{col 54}{space 4} .1051739{col 67}{space 3} .1511031
{txt}{space 6}3#AEP  {c |}{col 14}{res}{space 2} .1739162{col 26}{space 2} .0411707{col 37}{space 1}    4.22{col 46}{space 3}0.000{col 54}{space 4} .0932231{col 67}{space 3} .2546094
{txt}{space 4}4#Other  {c |}{col 14}{res}{space 2} .1943312{col 26}{space 2} .0115706{col 37}{space 1}   16.80{col 46}{space 3}0.000{col 54}{space 4} .1716532{col 67}{space 3} .2170091
{txt}{space 6}4#AEP  {c |}{col 14}{res}{space 2} .2993727{col 26}{space 2} .0364255{col 37}{space 1}    8.22{col 46}{space 3}0.000{col 54}{space 4} .2279801{col 67}{space 3} .3707653
{txt}{space 4}5#Other  {c |}{col 14}{res}{space 2} .1816276{col 26}{space 2} .0123861{col 37}{space 1}   14.66{col 46}{space 3}0.000{col 54}{space 4} .1573512{col 67}{space 3} .2059039
{txt}{space 6}5#AEP  {c |}{col 14}{res}{space 2}  .416323{col 26}{space 2}  .091974{col 37}{space 1}    4.53{col 46}{space 3}0.000{col 54}{space 4} .2360574{col 67}{space 3} .5965887
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. mplotoffset, offset(0.15) recast(connected) ci1opts(fintensity(30)) plotopt( msize(medium))  ///
> title("Great Britain", margin(0 0 5 0) size(medium)  position(12) span) ///
> ytitle("") xtitle("") saving(putin_uk, replace) scheme(538bw) ///
> plot( , label("Non-AEP" "AEP" )) legend(pos(10) row(2) ring(0)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) 

{text}{p 2 6 2}Variables that uniquely identify margins: year pr1{p_end}
{res}{txt}(file putin_uk.gph saved)

{com}.  
. *France
. logit PutinBi i.pr1##i.year female age agesq i.unedu   if ccode ==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:  -2156.05}  
Iteration 1:{space 3}log likelihood = {res:-2062.8898}  
Iteration 2:{space 3}log likelihood = {res:-2053.5997}  
Iteration 3:{space 3}log likelihood = {res:-2053.5799}  
Iteration 4:{space 3}log likelihood = {res:-2053.5799}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,958
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    204.94
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2053.5799{txt}{col 49}Pseudo R2{col 67}= {res}    0.0475

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .5431333{col 31}{space 2} .3546789{col 42}{space 1}    1.53{col 51}{space 3}0.126{col 59}{space 4}-.1520247{col 72}{space 3} 1.238291
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2} .2342973{col 31}{space 2} .1480777{col 42}{space 1}    1.58{col 51}{space 3}0.114{col 59}{space 4}-.0559296{col 72}{space 3} .5245243
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}  .261887{col 31}{space 2} .1477109{col 42}{space 1}    1.77{col 51}{space 3}0.076{col 59}{space 4}-.0276211{col 72}{space 3}  .551395
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .7612576{col 31}{space 2} .1393271{col 42}{space 1}    5.46{col 51}{space 3}0.000{col 59}{space 4} .4881815{col 72}{space 3} 1.034334
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .5070819{col 31}{space 2}  .142373{col 42}{space 1}    3.56{col 51}{space 3}0.000{col 59}{space 4}  .228036{col 72}{space 3} .7861279
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .2468782{col 31}{space 2} .4397594{col 42}{space 1}    0.56{col 51}{space 3}0.575{col 59}{space 4}-.6150345{col 72}{space 3} 1.108791
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .3657384{col 31}{space 2} .4359346{col 42}{space 1}    0.84{col 51}{space 3}0.401{col 59}{space 4}-.4886778{col 72}{space 3} 1.220155
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .2557511{col 31}{space 2} .4270502{col 42}{space 1}    0.60{col 51}{space 3}0.549{col 59}{space 4} -.581252{col 72}{space 3} 1.092754
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}  .994367{col 31}{space 2} .4370446{col 42}{space 1}    2.28{col 51}{space 3}0.023{col 59}{space 4} .1377753{col 72}{space 3} 1.850959
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.5231611{col 31}{space 2} .0812793{col 42}{space 1}   -6.44{col 51}{space 3}0.000{col 59}{space 4}-.6824657{col 72}{space 3}-.3638565
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0698521{col 31}{space 2} .0112449{col 42}{space 1}   -6.21{col 51}{space 3}0.000{col 59}{space 4}-.0918917{col 72}{space 3}-.0478126
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}  .000701{col 31}{space 2} .0001081{col 42}{space 1}    6.48{col 51}{space 3}0.000{col 59}{space 4} .0004891{col 72}{space 3}  .000913
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1623959{col 31}{space 2} .0837175{col 42}{space 1}   -1.94{col 51}{space 3}0.052{col 59}{space 4}-.3264791{col 72}{space 3} .0016873
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.3462383{col 31}{space 2} .2911723{col 42}{space 1}   -1.19{col 51}{space 3}0.234{col 59}{space 4}-.9169254{col 72}{space 3} .2244489
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins pr1, at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     4,958
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}_at#pr1 {c |}
{space 4}1#Other  {c |}{col 14}{res}{space 2} .1045437{col 26}{space 2} .0100978{col 37}{space 1}   10.35{col 46}{space 3}0.000{col 54}{space 4} .0847524{col 67}{space 3} .1243349
{txt}{space 6}1#AEP  {c |}{col 14}{res}{space 2} .1660469{col 26}{space 2} .0457571{col 37}{space 1}    3.63{col 46}{space 3}0.000{col 54}{space 4} .0763646{col 67}{space 3} .2557293
{txt}{space 4}2#Other  {c |}{col 14}{res}{space 2} .1281908{col 26}{space 2} .0109582{col 37}{space 1}   11.70{col 46}{space 3}0.000{col 54}{space 4} .1067132{col 67}{space 3} .1496684
{txt}{space 6}2#AEP  {c |}{col 14}{res}{space 2} .2416287{col 26}{space 2} .0432967{col 37}{space 1}    5.58{col 46}{space 3}0.000{col 54}{space 4} .1567688{col 67}{space 3} .3264886
{txt}{space 4}3#Other  {c |}{col 14}{res}{space 2} .1312529{col 26}{space 2} .0111019{col 37}{space 1}   11.82{col 46}{space 3}0.000{col 54}{space 4} .1094936{col 67}{space 3} .1530122
{txt}{space 6}3#AEP  {c |}{col 14}{res}{space 2} .2687084{col 26}{space 2} .0450136{col 37}{space 1}    5.97{col 46}{space 3}0.000{col 54}{space 4} .1804833{col 67}{space 3} .3569335
{txt}{space 4}4#Other  {c |}{col 14}{res}{space 2} .1977431{col 26}{space 2} .0133795{col 37}{space 1}   14.78{col 46}{space 3}0.000{col 54}{space 4} .1715198{col 67}{space 3} .2239663
{txt}{space 6}4#AEP  {c |}{col 14}{res}{space 2} .3490906{col 26}{space 2} .0492346{col 37}{space 1}    7.09{col 46}{space 3}0.000{col 54}{space 4} .2525925{col 67}{space 3} .4455886
{txt}{space 4}5#Other  {c |}{col 14}{res}{space 2}  .161212{col 26}{space 2} .0120533{col 37}{space 1}   13.37{col 46}{space 3}0.000{col 54}{space 4} .1375879{col 67}{space 3} .1848361
{txt}{space 6}5#AEP  {c |}{col 14}{res}{space 2} .4616154{col 26}{space 2} .0574755{col 37}{space 1}    8.03{col 46}{space 3}0.000{col 54}{space 4} .3489655{col 67}{space 3} .5742653
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. mplotoffset, offset(0.15) recast(connected) ci1opts(fintensity(30)) plotopt( msize(medium)) ///
> title("France", margin(0 0 5 0) size(medium)  position(12) span) ///
> ytitle("") xtitle("") saving(putin_fra, replace) scheme(538bw) ///
> plot( , label("Non-AEP" "AEP" )) legend(pos(10) row(2) ring(0)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) 

{text}{p 2 6 2}Variables that uniquely identify margins: year pr1{p_end}
{res}{txt}(file putin_fra.gph saved)

{com}. 
. *Germany
. logit PutinBi i.pr1##i.year female age agesq i.unedu  if ccode ==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2757.9254}  
Iteration 1:{space 3}log likelihood = {res:-2644.9441}  
Iteration 2:{space 3}log likelihood = {res:-2642.2455}  
Iteration 3:{space 3}log likelihood = {res:-2642.2434}  
Iteration 4:{space 3}log likelihood = {res:-2642.2434}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,901
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    231.36
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2642.2434{txt}{col 49}Pseudo R2{col 67}= {res}    0.0419

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}  .379474{col 31}{space 2} .3423538{col 42}{space 1}    1.11{col 51}{space 3}0.268{col 59}{space 4}-.2915272{col 72}{space 3} 1.050475
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.1069874{col 31}{space 2} .1166424{col 42}{space 1}   -0.92{col 51}{space 3}0.359{col 59}{space 4}-.3356022{col 72}{space 3} .1216275
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}  -.16243{col 31}{space 2} .1165885{col 42}{space 1}   -1.39{col 51}{space 3}0.164{col 59}{space 4}-.3909392{col 72}{space 3} .0660792
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .4025037{col 31}{space 2} .1090292{col 42}{space 1}    3.69{col 51}{space 3}0.000{col 59}{space 4} .1888104{col 72}{space 3} .6161969
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .2440432{col 31}{space 2} .1102381{col 42}{space 1}    2.21{col 51}{space 3}0.027{col 59}{space 4} .0279805{col 72}{space 3} .4601058
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .7695453{col 31}{space 2} .4264338{col 42}{space 1}    1.80{col 51}{space 3}0.071{col 59}{space 4}-.0662495{col 72}{space 3}  1.60534
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.325229{col 31}{space 2} .4326502{col 42}{space 1}    3.06{col 51}{space 3}0.002{col 59}{space 4} .4772506{col 72}{space 3} 2.173208
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .4325848{col 31}{space 2} .4307544{col 42}{space 1}    1.00{col 51}{space 3}0.315{col 59}{space 4}-.4116783{col 72}{space 3} 1.276848
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .2833297{col 31}{space 2}   .42641{col 42}{space 1}    0.66{col 51}{space 3}0.506{col 59}{space 4}-.5524185{col 72}{space 3} 1.119078
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.5977825{col 31}{space 2} .0690678{col 42}{space 1}   -8.66{col 51}{space 3}0.000{col 59}{space 4}-.7331529{col 72}{space 3} -.462412
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0364712{col 31}{space 2} .0105404{col 42}{space 1}   -3.46{col 51}{space 3}0.001{col 59}{space 4}  -.05713{col 72}{space 3}-.0158124
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0004075{col 31}{space 2} .0001012{col 42}{space 1}    4.03{col 51}{space 3}0.000{col 59}{space 4} .0002093{col 72}{space 3} .0006058
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0066655{col 31}{space 2} .0682685{col 42}{space 1}   -0.10{col 51}{space 3}0.922{col 59}{space 4}-.1404693{col 72}{space 3} .1271382
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.3231358{col 31}{space 2} .2696472{col 42}{space 1}   -1.20{col 51}{space 3}0.231{col 59}{space 4}-.8516346{col 72}{space 3}  .205363
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins pr1, at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     4,901
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}_at#pr1 {c |}
{space 4}1#Other  {c |}{col 14}{res}{space 2}  .220909{col 26}{space 2} .0135566{col 37}{space 1}   16.30{col 46}{space 3}0.000{col 54}{space 4} .1943386{col 67}{space 3} .2474794
{txt}{space 6}1#AEP  {c |}{col 14}{res}{space 2} .2912796{col 26}{space 2} .0670669{col 37}{space 1}    4.34{col 46}{space 3}0.000{col 54}{space 4} .1598309{col 67}{space 3} .4227283
{txt}{space 4}2#Other  {c |}{col 14}{res}{space 2} .2033808{col 26}{space 2}  .013481{col 37}{space 1}   15.09{col 46}{space 3}0.000{col 54}{space 4} .1769585{col 67}{space 3} .2298032
{txt}{space 6}2#AEP  {c |}{col 14}{res}{space 2} .4393222{col 26}{space 2} .0575151{col 37}{space 1}    7.64{col 46}{space 3}0.000{col 54}{space 4} .3265946{col 67}{space 3} .5520498
{txt}{space 4}3#Other  {c |}{col 14}{res}{space 2} .1947063{col 26}{space 2} .0130168{col 37}{space 1}   14.96{col 46}{space 3}0.000{col 54}{space 4} .1691938{col 67}{space 3} .2202187
{txt}{space 6}3#AEP  {c |}{col 14}{res}{space 2} .5602835{col 26}{space 2} .0601265{col 37}{space 1}    9.32{col 46}{space 3}0.000{col 54}{space 4} .4424376{col 67}{space 3} .6781293
{txt}{space 4}4#Other  {c |}{col 14}{res}{space 2} .2959425{col 26}{space 2} .0149413{col 37}{space 1}   19.81{col 46}{space 3}0.000{col 54}{space 4}  .266658{col 67}{space 3} .3252269
{txt}{space 6}4#AEP  {c |}{col 14}{res}{space 2} .4809544{col 26}{space 2}  .060942{col 37}{space 1}    7.89{col 46}{space 3}0.000{col 54}{space 4} .3615104{col 67}{space 3} .6003985
{txt}{space 4}5#Other  {c |}{col 14}{res}{space 2} .2647369{col 26}{space 2} .0143536{col 37}{space 1}   18.44{col 46}{space 3}0.000{col 54}{space 4} .2366043{col 67}{space 3} .2928695
{txt}{space 6}5#AEP  {c |}{col 14}{res}{space 2} .4072456{col 26}{space 2} .0569743{col 37}{space 1}    7.15{col 46}{space 3}0.000{col 54}{space 4}  .295578{col 67}{space 3} .5189131
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. mplotoffset, offset(0.15) recast(connected) ci1opts(fintensity(30)) plotopt( msize(medium)) ///
> title("Germany", margin(0 0 5 0) size(medium)  position(12) span) ///
> ytitle("") xtitle("") saving(putin_ger, replace) scheme(538bw) ///
> plot( , label("Non-AEP" "AEP" )) legend(pos(10) row(2) ring(0)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) 

{text}{p 2 6 2}Variables that uniquely identify margins: year pr1{p_end}
{res}{txt}(file putin_ger.gph saved)

{com}. 
. *Italy
. logit PutinBi i.pr1##i.year female age agesq i.unedu   if ccode ==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2505.1522}  
Iteration 1:{space 3}log likelihood = {res: -2434.478}  
Iteration 2:{space 3}log likelihood = {res: -2432.915}  
Iteration 3:{space 3}log likelihood = {res:-2432.9147}  
Iteration 4:{space 3}log likelihood = {res:-2432.9147}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,620
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    144.48
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2432.9147{txt}{col 49}Pseudo R2{col 67}= {res}    0.0288

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.4567092{col 31}{space 2} .5416035{col 42}{space 1}   -0.84{col 51}{space 3}0.399{col 59}{space 4}-1.518233{col 72}{space 3} .6048141
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.0551994{col 31}{space 2} .1252326{col 42}{space 1}   -0.44{col 51}{space 3}0.659{col 59}{space 4}-.3006507{col 72}{space 3}  .190252
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.0194584{col 31}{space 2} .1252693{col 42}{space 1}   -0.16{col 51}{space 3}0.877{col 59}{space 4}-.2649817{col 72}{space 3} .2260649
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}  .752149{col 31}{space 2} .1120999{col 42}{space 1}    6.71{col 51}{space 3}0.000{col 59}{space 4} .5324372{col 72}{space 3} .9718607
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .6254497{col 31}{space 2} .1188763{col 42}{space 1}    5.26{col 51}{space 3}0.000{col 59}{space 4} .3924564{col 72}{space 3} .8584431
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.807738{col 31}{space 2} .6570834{col 42}{space 1}    2.75{col 51}{space 3}0.006{col 59}{space 4} .5198783{col 72}{space 3} 3.095598
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .9663712{col 31}{space 2} .6385751{col 42}{space 1}    1.51{col 51}{space 3}0.130{col 59}{space 4} -.285213{col 72}{space 3} 2.217955
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} 1.123479{col 31}{space 2} .6006285{col 42}{space 1}    1.87{col 51}{space 3}0.061{col 59}{space 4}-.0537315{col 72}{space 3} 2.300689
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .5402807{col 31}{space 2} .6212583{col 42}{space 1}    0.87{col 51}{space 3}0.384{col 59}{space 4}-.6773632{col 72}{space 3} 1.757925
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.2581362{col 31}{space 2} .0711122{col 42}{space 1}   -3.63{col 51}{space 3}0.000{col 59}{space 4}-.3975135{col 72}{space 3}-.1187589
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0045472{col 31}{space 2} .0121013{col 42}{space 1}    0.38{col 51}{space 3}0.707{col 59}{space 4} -.019171{col 72}{space 3} .0282653
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}-.0000687{col 31}{space 2} .0001198{col 42}{space 1}   -0.57{col 51}{space 3}0.566{col 59}{space 4}-.0003035{col 72}{space 3}  .000166
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0443326{col 31}{space 2} .0812518{col 42}{space 1}    0.55{col 51}{space 3}0.585{col 59}{space 4} -.114918{col 72}{space 3} .2035833
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-1.425025{col 31}{space 2} .3024292{col 42}{space 1}   -4.71{col 51}{space 3}0.000{col 59}{space 4}-2.017775{col 72}{space 3}-.8322742
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins pr1, at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     4,620
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}_at#pr1 {c |}
{space 4}1#Other  {c |}{col 14}{res}{space 2} .1830483{col 26}{space 2} .0128364{col 37}{space 1}   14.26{col 46}{space 3}0.000{col 54}{space 4} .1578895{col 67}{space 3} .2082071
{txt}{space 6}1#AEP  {c |}{col 14}{res}{space 2} .1243981{col 26}{space 2} .0581768{col 37}{space 1}    2.14{col 46}{space 3}0.032{col 54}{space 4} .0103737{col 67}{space 3} .2384225
{txt}{space 4}2#Other  {c |}{col 14}{res}{space 2} .1749599{col 26}{space 2}  .012629{col 37}{space 1}   13.85{col 46}{space 3}0.000{col 54}{space 4} .1502074{col 67}{space 3} .1997123
{txt}{space 6}2#AEP  {c |}{col 14}{res}{space 2} .4489138{col 26}{space 2} .0892337{col 37}{space 1}    5.03{col 46}{space 3}0.000{col 54}{space 4}  .274019{col 67}{space 3} .6238085
{txt}{space 4}3#Other  {c |}{col 14}{res}{space 2} .1801645{col 26}{space 2} .0129637{col 37}{space 1}   13.90{col 46}{space 3}0.000{col 54}{space 4} .1547561{col 67}{space 3} .2055729
{txt}{space 6}3#AEP  {c |}{col 14}{res}{space 2} .2675217{col 26}{space 2} .0640104{col 37}{space 1}    4.18{col 46}{space 3}0.000{col 54}{space 4} .1420636{col 67}{space 3} .3929799
{txt}{space 4}4#Other  {c |}{col 14}{res}{space 2} .3216243{col 26}{space 2} .0165505{col 37}{space 1}   19.43{col 46}{space 3}0.000{col 54}{space 4} .2891859{col 67}{space 3} .3540627
{txt}{space 6}4#AEP  {c |}{col 14}{res}{space 2} .4793739{col 26}{space 2} .0618983{col 37}{space 1}    7.74{col 46}{space 3}0.000{col 54}{space 4} .3580555{col 67}{space 3} .6006923
{txt}{space 4}5#Other  {c |}{col 14}{res}{space 2} .2947334{col 26}{space 2}  .016595{col 37}{space 1}   17.76{col 46}{space 3}0.000{col 54}{space 4} .2622078{col 67}{space 3} .3272591
{txt}{space 6}5#AEP  {c |}{col 14}{res}{space 2} .3123259{col 26}{space 2} .0628356{col 37}{space 1}    4.97{col 46}{space 3}0.000{col 54}{space 4} .1891704{col 67}{space 3} .4354814
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. mplotoffset, offset(0.15) recast(connected) ci1opts(fintensity(30))  plotopt( msize(medium)) ///
> title("Italy", margin(0 0 5 0) size(medium)  position(12) span) ///
> ytitle("") xtitle("") saving(putin_ita, replace) scheme(538bw) ///
> plot( , label("Non-AEP" "AEP" )) legend(pos(10) row(2) ring(0)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) 

{text}{p 2 6 2}Variables that uniquely identify margins: year pr1{p_end}
{res}{txt}(file putin_ita.gph saved)

{com}. 
. grc1leg putin_uk.gph putin_fra.gph putin_ger.gph putin_ita.gph, ycommon iscale(0.8) l1("Predicted Probability", size(small)) /// 
> title(Confidence in Vladimir Putin) ring(0) position(1)
{res}{txt}
{com}. graph export "line_country_putin2", as(eps) replace
{txt}(file line_country_putin2 written in EPS format)

{com}. 
. 
. *United Kingdom
. logit  PutinBi i.pr1##i.year female age agesq i.unedu  if ccode ==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2434.9586}  
Iteration 1:{space 3}log likelihood = {res:-2374.5405}  
Iteration 2:{space 3}log likelihood = {res:-2372.7909}  
Iteration 3:{space 3}log likelihood = {res:  -2372.79}  
Iteration 4:{space 3}log likelihood = {res:  -2372.79}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,944
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    124.34
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}  -2372.79{txt}{col 49}Pseudo R2{col 67}= {res}    0.0255

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2098011{col 31}{space 2} .4008922{col 42}{space 1}   -0.52{col 51}{space 3}0.601{col 59}{space 4}-.9955354{col 72}{space 3} .5759333
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.0971277{col 31}{space 2} .1227587{col 42}{space 1}   -0.79{col 51}{space 3}0.429{col 59}{space 4}-.3377303{col 72}{space 3} .1434749
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.6806307{col 31}{space 2} .1359025{col 42}{space 1}   -5.01{col 51}{space 3}0.000{col 59}{space 4}-.9469946{col 72}{space 3}-.4142668
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} -.178709{col 31}{space 2}  .113303{col 42}{space 1}   -1.58{col 51}{space 3}0.115{col 59}{space 4}-.4007788{col 72}{space 3} .0433607
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.2631959{col 31}{space 2}  .120347{col 42}{space 1}   -2.19{col 51}{space 3}0.029{col 59}{space 4}-.4990718{col 72}{space 3}-.0273201
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2}  .784164{col 31}{space 2} .4713797{col 42}{space 1}    1.66{col 51}{space 3}0.096{col 59}{space 4}-.1397233{col 72}{space 3} 1.708051
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .5737192{col 31}{space 2} .5059506{col 42}{space 1}    1.13{col 51}{space 3}0.257{col 59}{space 4}-.4179258{col 72}{space 3} 1.565364
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .7916332{col 31}{space 2} .4440965{col 42}{space 1}    1.78{col 51}{space 3}0.075{col 59}{space 4}-.0787799{col 72}{space 3} 1.662046
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} 1.399278{col 31}{space 2} .5627975{col 42}{space 1}    2.49{col 51}{space 3}0.013{col 59}{space 4} .2962147{col 72}{space 3}  2.50234
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.4251279{col 31}{space 2} .0739813{col 42}{space 1}   -5.75{col 51}{space 3}0.000{col 59}{space 4}-.5701285{col 72}{space 3}-.2801273
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0397933{col 31}{space 2} .0105572{col 42}{space 1}   -3.77{col 51}{space 3}0.000{col 59}{space 4} -.060485{col 72}{space 3}-.0191017
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0002935{col 31}{space 2} .0000993{col 42}{space 1}    2.96{col 51}{space 3}0.003{col 59}{space 4}  .000099{col 72}{space 3}  .000488
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.2077077{col 31}{space 2} .0779683{col 42}{space 1}   -2.66{col 51}{space 3}0.008{col 59}{space 4}-.3605228{col 72}{space 3}-.0548926
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .2512954{col 31}{space 2} .2790165{col 42}{space 1}    0.90{col 51}{space 3}0.368{col 59}{space 4}-.2955668{col 72}{space 3} .7981577
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,944
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0337777{col 26}{space 2} .0610463{col 37}{space 1}   -0.55{col 46}{space 3}0.580{col 54}{space 4}-.1534263{col 67}{space 3} .0858709
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1076152{col 26}{space 2} .0511198{col 37}{space 1}    2.11{col 46}{space 3}0.035{col 54}{space 4} .0074223{col 67}{space 3} .2078081
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0457777{col 26}{space 2} .0427654{col 37}{space 1}    1.07{col 46}{space 3}0.284{col 54}{space 4} -.038041{col 67}{space 3} .1295964
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1050415{col 26}{space 2} .0381621{col 37}{space 1}    2.75{col 46}{space 3}0.006{col 54}{space 4} .0302451{col 67}{space 3} .1798379
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2346955{col 26}{space 2} .0927198{col 37}{space 1}    2.53{col 46}{space 3}0.011{col 54}{space 4}  .052968{col 67}{space 3} .4164229
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Great Britain", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") saving(uklinep, replace) yscale(range(-.1 0.3))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file uklinep.gph saved)

{com}. 
. *France
. logit PutinBi i.pr1##i.year female age agesq i.unedu  if ccode ==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:  -2156.05}  
Iteration 1:{space 3}log likelihood = {res:-2062.8898}  
Iteration 2:{space 3}log likelihood = {res:-2053.5997}  
Iteration 3:{space 3}log likelihood = {res:-2053.5799}  
Iteration 4:{space 3}log likelihood = {res:-2053.5799}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,958
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    204.94
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2053.5799{txt}{col 49}Pseudo R2{col 67}= {res}    0.0475

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .5431333{col 31}{space 2} .3546789{col 42}{space 1}    1.53{col 51}{space 3}0.126{col 59}{space 4}-.1520247{col 72}{space 3} 1.238291
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2} .2342973{col 31}{space 2} .1480777{col 42}{space 1}    1.58{col 51}{space 3}0.114{col 59}{space 4}-.0559296{col 72}{space 3} .5245243
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}  .261887{col 31}{space 2} .1477109{col 42}{space 1}    1.77{col 51}{space 3}0.076{col 59}{space 4}-.0276211{col 72}{space 3}  .551395
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .7612576{col 31}{space 2} .1393271{col 42}{space 1}    5.46{col 51}{space 3}0.000{col 59}{space 4} .4881815{col 72}{space 3} 1.034334
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .5070819{col 31}{space 2}  .142373{col 42}{space 1}    3.56{col 51}{space 3}0.000{col 59}{space 4}  .228036{col 72}{space 3} .7861279
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .2468782{col 31}{space 2} .4397594{col 42}{space 1}    0.56{col 51}{space 3}0.575{col 59}{space 4}-.6150345{col 72}{space 3} 1.108791
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .3657384{col 31}{space 2} .4359346{col 42}{space 1}    0.84{col 51}{space 3}0.401{col 59}{space 4}-.4886778{col 72}{space 3} 1.220155
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .2557511{col 31}{space 2} .4270502{col 42}{space 1}    0.60{col 51}{space 3}0.549{col 59}{space 4} -.581252{col 72}{space 3} 1.092754
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}  .994367{col 31}{space 2} .4370446{col 42}{space 1}    2.28{col 51}{space 3}0.023{col 59}{space 4} .1377753{col 72}{space 3} 1.850959
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.5231611{col 31}{space 2} .0812793{col 42}{space 1}   -6.44{col 51}{space 3}0.000{col 59}{space 4}-.6824657{col 72}{space 3}-.3638565
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0698521{col 31}{space 2} .0112449{col 42}{space 1}   -6.21{col 51}{space 3}0.000{col 59}{space 4}-.0918917{col 72}{space 3}-.0478126
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}  .000701{col 31}{space 2} .0001081{col 42}{space 1}    6.48{col 51}{space 3}0.000{col 59}{space 4} .0004891{col 72}{space 3}  .000913
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1623959{col 31}{space 2} .0837175{col 42}{space 1}   -1.94{col 51}{space 3}0.052{col 59}{space 4}-.3264791{col 72}{space 3} .0016873
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.3462383{col 31}{space 2} .2911723{col 42}{space 1}   -1.19{col 51}{space 3}0.234{col 59}{space 4}-.9169254{col 72}{space 3} .2244489
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,958
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0615033{col 26}{space 2} .0468771{col 37}{space 1}    1.31{col 46}{space 3}0.190{col 54}{space 4}-.0303742{col 67}{space 3} .1533808
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1134379{col 26}{space 2}  .044631{col 37}{space 1}    2.54{col 46}{space 3}0.011{col 54}{space 4} .0259627{col 67}{space 3} .2009132
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1374555{col 26}{space 2} .0463361{col 37}{space 1}    2.97{col 46}{space 3}0.003{col 54}{space 4} .0466384{col 67}{space 3} .2282726
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1513475{col 26}{space 2} .0510162{col 37}{space 1}    2.97{col 46}{space 3}0.003{col 54}{space 4} .0513576{col 67}{space 3} .2513374
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .3004034{col 26}{space 2}  .058718{col 37}{space 1}    5.12{col 46}{space 3}0.000{col 54}{space 4} .1853182{col 67}{space 3} .4154886
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("France", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") saving(francelinep, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file francelinep.gph saved)

{com}. 
. *Germany
. logit PutinBi i.pr1##i.year female age agesq i.unedu   if ccode ==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2757.9254}  
Iteration 1:{space 3}log likelihood = {res:-2644.9441}  
Iteration 2:{space 3}log likelihood = {res:-2642.2455}  
Iteration 3:{space 3}log likelihood = {res:-2642.2434}  
Iteration 4:{space 3}log likelihood = {res:-2642.2434}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,901
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    231.36
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2642.2434{txt}{col 49}Pseudo R2{col 67}= {res}    0.0419

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}  .379474{col 31}{space 2} .3423538{col 42}{space 1}    1.11{col 51}{space 3}0.268{col 59}{space 4}-.2915272{col 72}{space 3} 1.050475
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.1069874{col 31}{space 2} .1166424{col 42}{space 1}   -0.92{col 51}{space 3}0.359{col 59}{space 4}-.3356022{col 72}{space 3} .1216275
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}  -.16243{col 31}{space 2} .1165885{col 42}{space 1}   -1.39{col 51}{space 3}0.164{col 59}{space 4}-.3909392{col 72}{space 3} .0660792
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .4025037{col 31}{space 2} .1090292{col 42}{space 1}    3.69{col 51}{space 3}0.000{col 59}{space 4} .1888104{col 72}{space 3} .6161969
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .2440432{col 31}{space 2} .1102381{col 42}{space 1}    2.21{col 51}{space 3}0.027{col 59}{space 4} .0279805{col 72}{space 3} .4601058
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .7695453{col 31}{space 2} .4264338{col 42}{space 1}    1.80{col 51}{space 3}0.071{col 59}{space 4}-.0662495{col 72}{space 3}  1.60534
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.325229{col 31}{space 2} .4326502{col 42}{space 1}    3.06{col 51}{space 3}0.002{col 59}{space 4} .4772506{col 72}{space 3} 2.173208
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .4325848{col 31}{space 2} .4307544{col 42}{space 1}    1.00{col 51}{space 3}0.315{col 59}{space 4}-.4116783{col 72}{space 3} 1.276848
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .2833297{col 31}{space 2}   .42641{col 42}{space 1}    0.66{col 51}{space 3}0.506{col 59}{space 4}-.5524185{col 72}{space 3} 1.119078
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.5977825{col 31}{space 2} .0690678{col 42}{space 1}   -8.66{col 51}{space 3}0.000{col 59}{space 4}-.7331529{col 72}{space 3} -.462412
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0364712{col 31}{space 2} .0105404{col 42}{space 1}   -3.46{col 51}{space 3}0.001{col 59}{space 4}  -.05713{col 72}{space 3}-.0158124
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0004075{col 31}{space 2} .0001012{col 42}{space 1}    4.03{col 51}{space 3}0.000{col 59}{space 4} .0002093{col 72}{space 3} .0006058
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0066655{col 31}{space 2} .0682685{col 42}{space 1}   -0.10{col 51}{space 3}0.922{col 59}{space 4}-.1404693{col 72}{space 3} .1271382
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.3231358{col 31}{space 2} .2696472{col 42}{space 1}   -1.20{col 51}{space 3}0.231{col 59}{space 4}-.8516346{col 72}{space 3}  .205363
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,901
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0703705{col 26}{space 2} .0684303{col 37}{space 1}    1.03{col 46}{space 3}0.304{col 54}{space 4}-.0637505{col 67}{space 3} .2044915
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2359414{col 26}{space 2} .0591179{col 37}{space 1}    3.99{col 46}{space 3}0.000{col 54}{space 4} .1200725{col 67}{space 3} .3518103
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .3655772{col 26}{space 2} .0615259{col 37}{space 1}    5.94{col 46}{space 3}0.000{col 54}{space 4} .2449886{col 67}{space 3} .4861658
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .185012{col 26}{space 2} .0627089{col 37}{space 1}    2.95{col 46}{space 3}0.003{col 54}{space 4} .0621049{col 67}{space 3} .3079191
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1425087{col 26}{space 2} .0587481{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4} .0273646{col 67}{space 3} .2576528
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Germany", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") saving(germanylinep, replace) yscale(range(-.1 0.4))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file germanylinep.gph saved)

{com}. 
. *Italy
. logit PutinBi i.pr1##i.year female age agesq i.unedu if ccode ==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2505.1522}  
Iteration 1:{space 3}log likelihood = {res: -2434.478}  
Iteration 2:{space 3}log likelihood = {res: -2432.915}  
Iteration 3:{space 3}log likelihood = {res:-2432.9147}  
Iteration 4:{space 3}log likelihood = {res:-2432.9147}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,620
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    144.48
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2432.9147{txt}{col 49}Pseudo R2{col 67}= {res}    0.0288

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.4567092{col 31}{space 2} .5416035{col 42}{space 1}   -0.84{col 51}{space 3}0.399{col 59}{space 4}-1.518233{col 72}{space 3} .6048141
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.0551994{col 31}{space 2} .1252326{col 42}{space 1}   -0.44{col 51}{space 3}0.659{col 59}{space 4}-.3006507{col 72}{space 3}  .190252
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.0194584{col 31}{space 2} .1252693{col 42}{space 1}   -0.16{col 51}{space 3}0.877{col 59}{space 4}-.2649817{col 72}{space 3} .2260649
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}  .752149{col 31}{space 2} .1120999{col 42}{space 1}    6.71{col 51}{space 3}0.000{col 59}{space 4} .5324372{col 72}{space 3} .9718607
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .6254497{col 31}{space 2} .1188763{col 42}{space 1}    5.26{col 51}{space 3}0.000{col 59}{space 4} .3924564{col 72}{space 3} .8584431
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.807738{col 31}{space 2} .6570834{col 42}{space 1}    2.75{col 51}{space 3}0.006{col 59}{space 4} .5198783{col 72}{space 3} 3.095598
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .9663712{col 31}{space 2} .6385751{col 42}{space 1}    1.51{col 51}{space 3}0.130{col 59}{space 4} -.285213{col 72}{space 3} 2.217955
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} 1.123479{col 31}{space 2} .6006285{col 42}{space 1}    1.87{col 51}{space 3}0.061{col 59}{space 4}-.0537315{col 72}{space 3} 2.300689
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .5402807{col 31}{space 2} .6212583{col 42}{space 1}    0.87{col 51}{space 3}0.384{col 59}{space 4}-.6773632{col 72}{space 3} 1.757925
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.2581362{col 31}{space 2} .0711122{col 42}{space 1}   -3.63{col 51}{space 3}0.000{col 59}{space 4}-.3975135{col 72}{space 3}-.1187589
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0045472{col 31}{space 2} .0121013{col 42}{space 1}    0.38{col 51}{space 3}0.707{col 59}{space 4} -.019171{col 72}{space 3} .0282653
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}-.0000687{col 31}{space 2} .0001198{col 42}{space 1}   -0.57{col 51}{space 3}0.566{col 59}{space 4}-.0003035{col 72}{space 3}  .000166
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0443326{col 31}{space 2} .0812518{col 42}{space 1}    0.55{col 51}{space 3}0.585{col 59}{space 4} -.114918{col 72}{space 3} .2035833
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-1.425025{col 31}{space 2} .3024292{col 42}{space 1}   -4.71{col 51}{space 3}0.000{col 59}{space 4}-2.017775{col 72}{space 3}-.8322742
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,620
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0586502{col 26}{space 2} .0595044{col 37}{space 1}   -0.99{col 46}{space 3}0.324{col 54}{space 4}-.1752767{col 67}{space 3} .0579763
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2739539{col 26}{space 2} .0900269{col 37}{space 1}    3.04{col 46}{space 3}0.002{col 54}{space 4} .0975044{col 67}{space 3} .4504034
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0873572{col 26}{space 2} .0651599{col 37}{space 1}    1.34{col 46}{space 3}0.180{col 54}{space 4}-.0403538{col 67}{space 3} .2150683
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1577496{col 26}{space 2} .0639131{col 37}{space 1}    2.47{col 46}{space 3}0.014{col 54}{space 4} .0324821{col 67}{space 3}  .283017
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0175925{col 26}{space 2} .0649544{col 37}{space 1}    0.27{col 46}{space 3}0.787{col 54}{space 4}-.1097158{col 67}{space 3} .1449007
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Italy", margin(0 0 5 0) size(medium)  position(12) span)  scheme(538bw) ///
> ytitle("") saving(italylinep, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file italylinep.gph saved)

{com}.   
.   
. gr combine uklinep.gph francelinep.gph germanylinep.gph italylinep.gph, ycommon iscale(0.8) /// 
> l1("Marginal Effect", size(small)) title(Confidence in Vladimir Putin)
{res}{txt}
{com}. graph export "putin_dydx2", as(eps) replace
{txt}(file putin_dydx2 written in EPS format)

{com}. 
. 
. ****************************
. *Favorability toward Russia*
. ****************************
. 
. *United Kingdom
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu  if ccode ==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2590.6948}  
Iteration 1:{space 3}log likelihood = {res:-2442.3558}  
Iteration 2:{space 3}log likelihood = {res:-2440.8358}  
Iteration 3:{space 3}log likelihood = {res:-2440.8346}  
Iteration 4:{space 3}log likelihood = {res:-2440.8346}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,080
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    299.72
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2440.8346{txt}{col 49}Pseudo R2{col 67}= {res}    0.0578

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.3772472{col 31}{space 2} .3305464{col 42}{space 1}   -1.14{col 51}{space 3}0.254{col 59}{space 4}-1.025106{col 72}{space 3} .2706119
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0406138{col 31}{space 2} .1080521{col 42}{space 1}    0.38{col 51}{space 3}0.707{col 59}{space 4}-.1711645{col 72}{space 3} .2523921
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.8922894{col 31}{space 2} .1123983{col 42}{space 1}   -7.94{col 51}{space 3}0.000{col 59}{space 4}-1.112586{col 72}{space 3}-.6719927
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-1.293838{col 31}{space 2} .1204386{col 42}{space 1}  -10.74{col 51}{space 3}0.000{col 59}{space 4}-1.529894{col 72}{space 3}-1.057783
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.8213998{col 31}{space 2} .1081767{col 42}{space 1}   -7.59{col 51}{space 3}0.000{col 59}{space 4}-1.033422{col 72}{space 3}-.6093775
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .5718393{col 31}{space 2} .4068896{col 42}{space 1}    1.41{col 51}{space 3}0.160{col 59}{space 4}-.2256496{col 72}{space 3} 1.369328
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .6480467{col 31}{space 2} .4201326{col 42}{space 1}    1.54{col 51}{space 3}0.123{col 59}{space 4}-.1753982{col 72}{space 3} 1.471491
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .7750749{col 31}{space 2} .4386767{col 42}{space 1}    1.77{col 51}{space 3}0.077{col 59}{space 4}-.0847157{col 72}{space 3} 1.634865
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .8227637{col 31}{space 2} .5451768{col 42}{space 1}    1.51{col 51}{space 3}0.131{col 59}{space 4}-.2457633{col 72}{space 3} 1.891291
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.3046417{col 31}{space 2} .0695255{col 42}{space 1}   -4.38{col 51}{space 3}0.000{col 59}{space 4}-.4409092{col 72}{space 3}-.1683742
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0586194{col 31}{space 2} .0103881{col 42}{space 1}   -5.64{col 51}{space 3}0.000{col 59}{space 4}-.0789797{col 72}{space 3}-.0382591
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0004537{col 31}{space 2} .0000992{col 42}{space 1}    4.57{col 51}{space 3}0.000{col 59}{space 4} .0002592{col 72}{space 3} .0006481
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1021148{col 31}{space 2} .0757489{col 42}{space 1}   -1.35{col 51}{space 3}0.178{col 59}{space 4}  -.25058{col 72}{space 3} .0463504
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.736742{col 31}{space 2}  .270469{col 42}{space 1}    6.42{col 51}{space 3}0.000{col 59}{space 4} 1.206633{col 72}{space 3} 2.266852
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins pr1, at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     4,080
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}_at#pr1 {c |}
{space 4}1#Other  {c |}{col 14}{res}{space 2} .4602284{col 26}{space 2} .0180497{col 37}{space 1}   25.50{col 46}{space 3}0.000{col 54}{space 4} .4248517{col 67}{space 3} .4956052
{txt}{space 6}1#AEP  {c |}{col 14}{res}{space 2} .3709165{col 26}{space 2} .0735509{col 37}{space 1}    5.04{col 46}{space 3}0.000{col 54}{space 4} .2267593{col 67}{space 3} .5150737
{txt}{space 4}2#Other  {c |}{col 14}{res}{space 2} .4701082{col 26}{space 2} .0192167{col 37}{space 1}   24.46{col 46}{space 3}0.000{col 54}{space 4} .4324442{col 67}{space 3} .5077721
{txt}{space 6}2#AEP  {c |}{col 14}{res}{space 2} .5176465{col 26}{space 2} .0548396{col 37}{space 1}    9.44{col 46}{space 3}0.000{col 54}{space 4} .4101628{col 67}{space 3} .6251302
{txt}{space 4}3#Other  {c |}{col 14}{res}{space 2}  .262597{col 26}{space 2} .0161657{col 37}{space 1}   16.24{col 46}{space 3}0.000{col 54}{space 4} .2309127{col 67}{space 3} .2942812
{txt}{space 6}3#AEP  {c |}{col 14}{res}{space 2} .3170743{col 26}{space 2}  .052088{col 37}{space 1}    6.09{col 46}{space 3}0.000{col 54}{space 4} .2149837{col 67}{space 3} .4191649
{txt}{space 4}4#Other  {c |}{col 14}{res}{space 2} .1936065{col 26}{space 2} .0144078{col 37}{space 1}   13.44{col 46}{space 3}0.000{col 54}{space 4} .1653678{col 67}{space 3} .2218452
{txt}{space 6}4#AEP  {c |}{col 14}{res}{space 2} .2618911{col 26}{space 2} .0518687{col 37}{space 1}    5.05{col 46}{space 3}0.000{col 54}{space 4} .1602304{col 67}{space 3} .3635519
{txt}{space 4}5#Other  {c |}{col 14}{res}{space 2} .2762713{col 26}{space 2} .0151747{col 37}{space 1}   18.21{col 46}{space 3}0.000{col 54}{space 4} .2465294{col 67}{space 3} .3060131
{txt}{space 6}5#AEP  {c |}{col 14}{res}{space 2} .3712278{col 26}{space 2} .0976306{col 37}{space 1}    3.80{col 46}{space 3}0.000{col 54}{space 4} .1798753{col 67}{space 3} .5625803
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. mplotoffset, offset(0.15) recast(connected) ci1opts(fintensity(30)) plotopt( msize(medium))  ///
> title("Great Britain", margin(0 0 5 0) size(medium)  position(12) span) ///
> ytitle("") xtitle("") saving(putin_uk, replace) scheme(538bw) ///
> plot( , label("Non-AEP" "AEP" )) legend(pos(10) row(2) ring(0)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) 

{text}{p 2 6 2}Variables that uniquely identify margins: year pr1{p_end}
{res}{txt}(file putin_uk.gph saved)

{com}. 
. *France
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu   if ccode ==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3082.5389}  
Iteration 1:{space 3}log likelihood = {res:-2957.1622}  
Iteration 2:{space 3}log likelihood = {res:-2955.9862}  
Iteration 3:{space 3}log likelihood = {res:-2955.9858}  
Iteration 4:{space 3}log likelihood = {res:-2955.9858}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,978
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    253.11
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2955.9858{txt}{col 49}Pseudo R2{col 67}= {res}    0.0411

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .5365231{col 31}{space 2}   .26923{col 42}{space 1}    1.99{col 51}{space 3}0.046{col 59}{space 4} .0088419{col 72}{space 3} 1.064204
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0127886{col 31}{space 2} .1015006{col 42}{space 1}    0.13{col 51}{space 3}0.900{col 59}{space 4} -.186149{col 72}{space 3} .2117262
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.5698942{col 31}{space 2} .1084513{col 42}{space 1}   -5.25{col 51}{space 3}0.000{col 59}{space 4}-.7824547{col 72}{space 3}-.3573336
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.3087298{col 31}{space 2} .1049462{col 42}{space 1}   -2.94{col 51}{space 3}0.003{col 59}{space 4}-.5144205{col 72}{space 3} -.103039
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .0824445{col 31}{space 2} .1014932{col 42}{space 1}    0.81{col 51}{space 3}0.417{col 59}{space 4}-.1164785{col 72}{space 3} .2813675
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.1111923{col 31}{space 2} .3516018{col 42}{space 1}   -0.32{col 51}{space 3}0.752{col 59}{space 4}-.8003193{col 72}{space 3} .5779346
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .0120262{col 31}{space 2} .3579198{col 42}{space 1}    0.03{col 51}{space 3}0.973{col 59}{space 4}-.6894836{col 72}{space 3} .7135361
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .3102993{col 31}{space 2} .3499358{col 42}{space 1}    0.89{col 51}{space 3}0.375{col 59}{space 4}-.3755623{col 72}{space 3} .9961609
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .6214469{col 31}{space 2} .3703837{col 42}{space 1}    1.68{col 51}{space 3}0.093{col 59}{space 4}-.1044918{col 72}{space 3} 1.347386
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.3916533{col 31}{space 2} .0631293{col 42}{space 1}   -6.20{col 51}{space 3}0.000{col 59}{space 4}-.5153845{col 72}{space 3}-.2679221
{txt}{space 14}age {c |}{col 19}{res}{space 2} -.069363{col 31}{space 2}  .009065{col 42}{space 1}   -7.65{col 51}{space 3}0.000{col 59}{space 4}  -.08713{col 72}{space 3} -.051596
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005824{col 31}{space 2} .0000885{col 42}{space 1}    6.58{col 51}{space 3}0.000{col 59}{space 4} .0004088{col 72}{space 3} .0007559
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} -.343497{col 31}{space 2} .0661803{col 42}{space 1}   -5.19{col 51}{space 3}0.000{col 59}{space 4}-.4732081{col 72}{space 3}-.2137859
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.437398{col 31}{space 2} .2294726{col 42}{space 1}    6.26{col 51}{space 3}0.000{col 59}{space 4} .9876396{col 72}{space 3} 1.887156
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins pr1, at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     4,978
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}_at#pr1 {c |}
{space 4}1#Other  {c |}{col 14}{res}{space 2} .3278976{col 26}{space 2} .0153251{col 37}{space 1}   21.40{col 46}{space 3}0.000{col 54}{space 4}  .297861{col 67}{space 3} .3579342
{txt}{space 6}1#AEP  {c |}{col 14}{res}{space 2} .4505032{col 26}{space 2} .0620718{col 37}{space 1}    7.26{col 46}{space 3}0.000{col 54}{space 4} .3288446{col 67}{space 3} .5721618
{txt}{space 4}2#Other  {c |}{col 14}{res}{space 2} .3306335{col 26}{space 2} .0153051{col 37}{space 1}   21.60{col 46}{space 3}0.000{col 54}{space 4}  .300636{col 67}{space 3}  .360631
{txt}{space 6}2#AEP  {c |}{col 14}{res}{space 2} .4270789{col 26}{space 2}   .05116{col 37}{space 1}    8.35{col 46}{space 3}0.000{col 54}{space 4} .3268072{col 67}{space 3} .5273505
{txt}{space 4}3#Other  {c |}{col 14}{res}{space 2} .2190541{col 26}{space 2} .0134974{col 37}{space 1}   16.23{col 46}{space 3}0.000{col 54}{space 4} .1925996{col 67}{space 3} .2455086
{txt}{space 6}3#AEP  {c |}{col 14}{res}{space 2} .3233566{col 26}{space 2} .0472331{col 37}{space 1}    6.85{col 46}{space 3}0.000{col 54}{space 4} .2307814{col 67}{space 3} .4159318
{txt}{space 4}4#Other  {c |}{col 14}{res}{space 2} .2655804{col 26}{space 2} .0144429{col 37}{space 1}   18.39{col 46}{space 3}0.000{col 54}{space 4} .2372729{col 67}{space 3} .2938879
{txt}{space 6}4#AEP  {c |}{col 14}{res}{space 2} .4508789{col 26}{space 2} .0506412{col 37}{space 1}    8.90{col 46}{space 3}0.000{col 54}{space 4} .3516239{col 67}{space 3} .5501339
{txt}{space 4}5#Other  {c |}{col 14}{res}{space 2} .3457294{col 26}{space 2} .0157382{col 37}{space 1}   21.97{col 46}{space 3}0.000{col 54}{space 4}  .314883{col 67}{space 3} .3765757
{txt}{space 6}5#AEP  {c |}{col 14}{res}{space 2} .6183785{col 26}{space 2} .0559583{col 37}{space 1}   11.05{col 46}{space 3}0.000{col 54}{space 4} .5087022{col 67}{space 3} .7280548
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. mplotoffset, offset(0.15) recast(connected) ci1opts(fintensity(30)) plotopt( msize(medium)) ///
> title("France", margin(0 0 5 0) size(medium)  position(12) span) ///
> ytitle("") xtitle("") saving(putin_fra, replace) scheme(538bw) ///
> plot( , label("Non-AEP" "AEP" )) legend(pos(10) row(2) ring(0)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) 

{text}{p 2 6 2}Variables that uniquely identify margins: year pr1{p_end}
{res}{txt}(file putin_fra.gph saved)

{com}. 
. *Germany
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu  if ccode ==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2846.8328}  
Iteration 1:{space 3}log likelihood = {res:-2729.2372}  
Iteration 2:{space 3}log likelihood = {res:-2727.4391}  
Iteration 3:{space 3}log likelihood = {res:-2727.4379}  
Iteration 4:{space 3}log likelihood = {res:-2727.4379}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,802
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    238.79
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2727.4379{txt}{col 49}Pseudo R2{col 67}= {res}    0.0419

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .4792311{col 31}{space 2} .3252968{col 42}{space 1}    1.47{col 51}{space 3}0.141{col 59}{space 4}-.1583388{col 72}{space 3} 1.116801
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.0683836{col 31}{space 2} .1028438{col 42}{space 1}   -0.66{col 51}{space 3}0.506{col 59}{space 4}-.2699538{col 72}{space 3} .1331867
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.7205554{col 31}{space 2} .1126692{col 42}{space 1}   -6.40{col 51}{space 3}0.000{col 59}{space 4} -.941383{col 72}{space 3}-.4997278
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.4486365{col 31}{space 2} .1073144{col 42}{space 1}   -4.18{col 51}{space 3}0.000{col 59}{space 4}-.6589688{col 72}{space 3}-.2383042
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.2088302{col 31}{space 2} .1045615{col 42}{space 1}   -2.00{col 51}{space 3}0.046{col 59}{space 4}-.4137669{col 72}{space 3}-.0038935
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.0845691{col 31}{space 2} .4707738{col 42}{space 1}   -0.18{col 51}{space 3}0.857{col 59}{space 4}-1.007269{col 72}{space 3} .8381305
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .4505122{col 31}{space 2} .4192124{col 42}{space 1}    1.07{col 51}{space 3}0.283{col 59}{space 4}-.3711291{col 72}{space 3} 1.272153
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .9794297{col 31}{space 2} .4177922{col 42}{space 1}    2.34{col 51}{space 3}0.019{col 59}{space 4}  .160572{col 72}{space 3} 1.798287
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}-.1923841{col 31}{space 2} .4195745{col 42}{space 1}   -0.46{col 51}{space 3}0.647{col 59}{space 4}-1.014735{col 72}{space 3} .6299668
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.6705885{col 31}{space 2} .0673927{col 42}{space 1}   -9.95{col 51}{space 3}0.000{col 59}{space 4}-.8026757{col 72}{space 3}-.5385013
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0521879{col 31}{space 2} .0101505{col 42}{space 1}   -5.14{col 51}{space 3}0.000{col 59}{space 4}-.0720825{col 72}{space 3}-.0322933
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005213{col 31}{space 2}  .000099{col 42}{space 1}    5.27{col 51}{space 3}0.000{col 59}{space 4} .0003273{col 72}{space 3} .0007153
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0057898{col 31}{space 2} .0677422{col 42}{space 1}    0.09{col 51}{space 3}0.932{col 59}{space 4}-.1269826{col 72}{space 3} .1385621
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .7207566{col 31}{space 2} .2540361{col 42}{space 1}    2.84{col 51}{space 3}0.005{col 59}{space 4} .2228551{col 72}{space 3} 1.218658
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins pr1, at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     4,802
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}_at#pr1 {c |}
{space 4}1#Other  {c |}{col 14}{res}{space 2} .3252061{col 26}{space 2} .0152842{col 37}{space 1}   21.28{col 46}{space 3}0.000{col 54}{space 4} .2952496{col 67}{space 3} .3551627
{txt}{space 6}1#AEP  {c |}{col 14}{res}{space 2} .4337104{col 26}{space 2} .0752101{col 37}{space 1}    5.77{col 46}{space 3}0.000{col 54}{space 4} .2863014{col 67}{space 3} .5811195
{txt}{space 4}2#Other  {c |}{col 14}{res}{space 2} .3108373{col 26}{space 2}  .015171{col 37}{space 1}   20.49{col 46}{space 3}0.000{col 54}{space 4} .2811027{col 67}{space 3}  .340572
{txt}{space 6}2#AEP  {c |}{col 14}{res}{space 2} .3978583{col 26}{space 2} .0770485{col 37}{space 1}    5.16{col 46}{space 3}0.000{col 54}{space 4} .2468461{col 67}{space 3} .5488706
{txt}{space 4}3#Other  {c |}{col 14}{res}{space 2} .1929539{col 26}{space 2}  .013225{col 37}{space 1}   14.59{col 46}{space 3}0.000{col 54}{space 4} .1670334{col 67}{space 3} .2188744
{txt}{space 6}3#AEP  {c |}{col 14}{res}{space 2} .3710914{col 26}{space 2} .0564522{col 37}{space 1}    6.57{col 46}{space 3}0.000{col 54}{space 4} .2604472{col 67}{space 3} .4817355
{txt}{space 4}4#Other  {c |}{col 14}{res}{space 2} .2376455{col 26}{space 2} .0140686{col 37}{space 1}   16.89{col 46}{space 3}0.000{col 54}{space 4} .2100716{col 67}{space 3} .2652194
{txt}{space 6}4#AEP  {c |}{col 14}{res}{space 2} .5610372{col 26}{space 2} .0594253{col 37}{space 1}    9.44{col 46}{space 3}0.000{col 54}{space 4} .4445657{col 67}{space 3} .6775087
{txt}{space 4}5#Other  {c |}{col 14}{res}{space 2} .2824274{col 26}{space 2} .0149205{col 37}{space 1}   18.93{col 46}{space 3}0.000{col 54}{space 4} .2531836{col 67}{space 3} .3116711
{txt}{space 6}5#AEP  {c |}{col 14}{res}{space 2}  .341996{col 26}{space 2} .0553067{col 37}{space 1}    6.18{col 46}{space 3}0.000{col 54}{space 4} .2335967{col 67}{space 3} .4503952
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. mplotoffset, offset(0.15) recast(connected) ci1opts(fintensity(30)) plotopt( msize(medium)) ///
> title("Germany", margin(0 0 5 0) size(medium)  position(12) span) ///
> ytitle("") xtitle("") saving(putin_ger, replace) scheme(538bw) ///
> plot( , label("Non-AEP" "AEP" )) legend(pos(10) row(2) ring(0)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) 

{text}{p 2 6 2}Variables that uniquely identify margins: year pr1{p_end}
{res}{txt}(file putin_ger.gph saved)

{com}. 
. *Italy
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu   if ccode ==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2828.0253}  
Iteration 1:{space 3}log likelihood = {res:-2765.9931}  
Iteration 2:{space 3}log likelihood = {res:-2765.4747}  
Iteration 3:{space 3}log likelihood = {res:-2765.4742}  
Iteration 4:{space 3}log likelihood = {res:-2765.4742}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,681
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    125.10
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2765.4742{txt}{col 49}Pseudo R2{col 67}= {res}    0.0221

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} -.784342{col 31}{space 2} .5430626{col 42}{space 1}   -1.44{col 51}{space 3}0.149{col 59}{space 4}-1.848725{col 72}{space 3} .2800412
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .4953104{col 31}{space 2} .1004309{col 42}{space 1}    4.93{col 51}{space 3}0.000{col 59}{space 4} .2984695{col 72}{space 3} .6921513
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.2452439{col 31}{space 2} .1153378{col 42}{space 1}   -2.13{col 51}{space 3}0.033{col 59}{space 4}-.4713019{col 72}{space 3}-.0191859
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}  .085253{col 31}{space 2} .1108525{col 42}{space 1}    0.77{col 51}{space 3}0.442{col 59}{space 4}-.1320139{col 72}{space 3} .3025199
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .6381002{col 31}{space 2} .1088526{col 42}{space 1}    5.86{col 51}{space 3}0.000{col 59}{space 4} .4247531{col 72}{space 3} .8514473
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} 1.120981{col 31}{space 2} .7035113{col 42}{space 1}    1.59{col 51}{space 3}0.111{col 59}{space 4}-.2578757{col 72}{space 3} 2.499838
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.523363{col 31}{space 2} .6640351{col 42}{space 1}    2.29{col 51}{space 3}0.022{col 59}{space 4} .2218783{col 72}{space 3} 2.824848
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.291839{col 31}{space 2} .6245787{col 42}{space 1}    2.07{col 51}{space 3}0.039{col 59}{space 4}  .067687{col 72}{space 3}  2.51599
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .7725306{col 31}{space 2} .6211774{col 42}{space 1}    1.24{col 51}{space 3}0.214{col 59}{space 4}-.4449547{col 72}{space 3} 1.990016
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.1220961{col 31}{space 2} .0653589{col 42}{space 1}   -1.87{col 51}{space 3}0.062{col 59}{space 4}-.2501972{col 72}{space 3} .0060051
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0124937{col 31}{space 2} .0109694{col 42}{space 1}   -1.14{col 51}{space 3}0.255{col 59}{space 4}-.0339934{col 72}{space 3} .0090059
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0000564{col 31}{space 2} .0001104{col 42}{space 1}    0.51{col 51}{space 3}0.609{col 59}{space 4}  -.00016{col 72}{space 3} .0002728
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .1164204{col 31}{space 2}  .074294{col 42}{space 1}    1.57{col 51}{space 3}0.117{col 59}{space 4}-.0291932{col 72}{space 3}  .262034
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.6263182{col 31}{space 2} .2684915{col 42}{space 1}   -2.33{col 51}{space 3}0.020{col 59}{space 4}-1.152552{col 72}{space 3}-.1000844
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins pr1, at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     4,681
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}_at#pr1 {c |}
{space 4}1#Other  {c |}{col 14}{res}{space 2} .2505445{col 26}{space 2} .0143266{col 37}{space 1}   17.49{col 46}{space 3}0.000{col 54}{space 4} .2224649{col 67}{space 3}  .278624
{txt}{space 6}1#AEP  {c |}{col 14}{res}{space 2} .1327052{col 26}{space 2} .0617508{col 37}{space 1}    2.15{col 46}{space 3}0.032{col 54}{space 4}  .011676{col 67}{space 3} .2537345
{txt}{space 4}2#Other  {c |}{col 14}{res}{space 2} .3536954{col 26}{space 2} .0157591{col 37}{space 1}   22.44{col 46}{space 3}0.000{col 54}{space 4}  .322808{col 67}{space 3} .3845827
{txt}{space 6}2#AEP  {c |}{col 14}{res}{space 2} .4333822{col 26}{space 2} .1078965{col 37}{space 1}    4.02{col 46}{space 3}0.000{col 54}{space 4}  .221909{col 67}{space 3} .6448554
{txt}{space 4}3#Other  {c |}{col 14}{res}{space 2}  .207521{col 26}{space 2} .0136369{col 37}{space 1}   15.22{col 46}{space 3}0.000{col 54}{space 4} .1807931{col 67}{space 3} .2342489
{txt}{space 6}3#AEP  {c |}{col 14}{res}{space 2} .3533469{col 26}{space 2} .0849502{col 37}{space 1}    4.16{col 46}{space 3}0.000{col 54}{space 4} .1868475{col 67}{space 3} .5198463
{txt}{space 4}4#Other  {c |}{col 14}{res}{space 2}  .266815{col 26}{space 2}  .014986{col 37}{space 1}   17.80{col 46}{space 3}0.000{col 54}{space 4} .2374429{col 67}{space 3} .2961871
{txt}{space 6}4#AEP  {c |}{col 14}{res}{space 2} .3761497{col 26}{space 2}  .070039{col 37}{space 1}    5.37{col 46}{space 3}0.000{col 54}{space 4} .2388758{col 67}{space 3} .5134237
{txt}{space 4}5#Other  {c |}{col 14}{res}{space 2} .3867907{col 26}{space 2} .0177637{col 37}{space 1}   21.77{col 46}{space 3}0.000{col 54}{space 4} .3519744{col 67}{space 3}  .421607
{txt}{space 6}5#AEP  {c |}{col 14}{res}{space 2} .3840085{col 26}{space 2}  .068768{col 37}{space 1}    5.58{col 46}{space 3}0.000{col 54}{space 4} .2492256{col 67}{space 3} .5187913
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. mplotoffset, offset(0.15) recast(connected) ci1opts(fintensity(30))  plotopt( msize(medium)) ///
> title("Italy", margin(0 0 5 0) size(medium)  position(12) span) ///
> ytitle("") xtitle("") saving(putin_ita, replace) scheme(538bw) ///
> plot( , label("Non-AEP" "AEP" )) legend(pos(10) row(2) ring(0)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) 

{text}{p 2 6 2}Variables that uniquely identify margins: year pr1{p_end}
{res}{txt}(file putin_ita.gph saved)

{com}. 
. grc1leg putin_uk.gph putin_fra.gph putin_ger.gph putin_ita.gph, ycommon iscale(0.8) l1("Predicted Probability", size(small)) /// 
> title(Favorability toward Russia) ring(0) position(1)
{res}{txt}
{com}. graph export "line_country_russia2", as(eps) replace
{txt}(file line_country_russia2 written in EPS format)

{com}. 
. *United Kingdom
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu if ccode ==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2590.6948}  
Iteration 1:{space 3}log likelihood = {res:-2442.3558}  
Iteration 2:{space 3}log likelihood = {res:-2440.8358}  
Iteration 3:{space 3}log likelihood = {res:-2440.8346}  
Iteration 4:{space 3}log likelihood = {res:-2440.8346}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,080
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    299.72
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2440.8346{txt}{col 49}Pseudo R2{col 67}= {res}    0.0578

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.3772472{col 31}{space 2} .3305464{col 42}{space 1}   -1.14{col 51}{space 3}0.254{col 59}{space 4}-1.025106{col 72}{space 3} .2706119
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0406138{col 31}{space 2} .1080521{col 42}{space 1}    0.38{col 51}{space 3}0.707{col 59}{space 4}-.1711645{col 72}{space 3} .2523921
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.8922894{col 31}{space 2} .1123983{col 42}{space 1}   -7.94{col 51}{space 3}0.000{col 59}{space 4}-1.112586{col 72}{space 3}-.6719927
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-1.293838{col 31}{space 2} .1204386{col 42}{space 1}  -10.74{col 51}{space 3}0.000{col 59}{space 4}-1.529894{col 72}{space 3}-1.057783
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.8213998{col 31}{space 2} .1081767{col 42}{space 1}   -7.59{col 51}{space 3}0.000{col 59}{space 4}-1.033422{col 72}{space 3}-.6093775
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .5718393{col 31}{space 2} .4068896{col 42}{space 1}    1.41{col 51}{space 3}0.160{col 59}{space 4}-.2256496{col 72}{space 3} 1.369328
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .6480467{col 31}{space 2} .4201326{col 42}{space 1}    1.54{col 51}{space 3}0.123{col 59}{space 4}-.1753982{col 72}{space 3} 1.471491
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .7750749{col 31}{space 2} .4386767{col 42}{space 1}    1.77{col 51}{space 3}0.077{col 59}{space 4}-.0847157{col 72}{space 3} 1.634865
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .8227637{col 31}{space 2} .5451768{col 42}{space 1}    1.51{col 51}{space 3}0.131{col 59}{space 4}-.2457633{col 72}{space 3} 1.891291
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.3046417{col 31}{space 2} .0695255{col 42}{space 1}   -4.38{col 51}{space 3}0.000{col 59}{space 4}-.4409092{col 72}{space 3}-.1683742
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0586194{col 31}{space 2} .0103881{col 42}{space 1}   -5.64{col 51}{space 3}0.000{col 59}{space 4}-.0789797{col 72}{space 3}-.0382591
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0004537{col 31}{space 2} .0000992{col 42}{space 1}    4.57{col 51}{space 3}0.000{col 59}{space 4} .0002592{col 72}{space 3} .0006481
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1021148{col 31}{space 2} .0757489{col 42}{space 1}   -1.35{col 51}{space 3}0.178{col 59}{space 4}  -.25058{col 72}{space 3} .0463504
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.736742{col 31}{space 2}  .270469{col 42}{space 1}    6.42{col 51}{space 3}0.000{col 59}{space 4} 1.206633{col 72}{space 3} 2.266852
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,080
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} -.089312{col 26}{space 2} .0756956{col 37}{space 1}   -1.18{col 46}{space 3}0.238{col 54}{space 4}-.2376727{col 67}{space 3} .0590488
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0475384{col 26}{space 2} .0581022{col 37}{space 1}    0.82{col 46}{space 3}0.413{col 54}{space 4}-.0663398{col 67}{space 3} .1614165
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0544773{col 26}{space 2} .0545086{col 37}{space 1}    1.00{col 46}{space 3}0.318{col 54}{space 4}-.0523576{col 67}{space 3} .1613122
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0682846{col 26}{space 2} .0537247{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0370138{col 67}{space 3} .1735831
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0949565{col 26}{space 2} .0986328{col 37}{space 1}    0.96{col 46}{space 3}0.336{col 54}{space 4}-.0983603{col 67}{space 3} .2882733
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Great Britain", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") xtitle("") saving(ukline, replace) yscale(range(-.1 0.3))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file ukline.gph saved)

{com}.  
. *France
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu  if ccode ==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3082.5389}  
Iteration 1:{space 3}log likelihood = {res:-2957.1622}  
Iteration 2:{space 3}log likelihood = {res:-2955.9862}  
Iteration 3:{space 3}log likelihood = {res:-2955.9858}  
Iteration 4:{space 3}log likelihood = {res:-2955.9858}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,978
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    253.11
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2955.9858{txt}{col 49}Pseudo R2{col 67}= {res}    0.0411

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .5365231{col 31}{space 2}   .26923{col 42}{space 1}    1.99{col 51}{space 3}0.046{col 59}{space 4} .0088419{col 72}{space 3} 1.064204
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0127886{col 31}{space 2} .1015006{col 42}{space 1}    0.13{col 51}{space 3}0.900{col 59}{space 4} -.186149{col 72}{space 3} .2117262
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.5698942{col 31}{space 2} .1084513{col 42}{space 1}   -5.25{col 51}{space 3}0.000{col 59}{space 4}-.7824547{col 72}{space 3}-.3573336
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.3087298{col 31}{space 2} .1049462{col 42}{space 1}   -2.94{col 51}{space 3}0.003{col 59}{space 4}-.5144205{col 72}{space 3} -.103039
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .0824445{col 31}{space 2} .1014932{col 42}{space 1}    0.81{col 51}{space 3}0.417{col 59}{space 4}-.1164785{col 72}{space 3} .2813675
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.1111923{col 31}{space 2} .3516018{col 42}{space 1}   -0.32{col 51}{space 3}0.752{col 59}{space 4}-.8003193{col 72}{space 3} .5779346
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .0120262{col 31}{space 2} .3579198{col 42}{space 1}    0.03{col 51}{space 3}0.973{col 59}{space 4}-.6894836{col 72}{space 3} .7135361
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .3102993{col 31}{space 2} .3499358{col 42}{space 1}    0.89{col 51}{space 3}0.375{col 59}{space 4}-.3755623{col 72}{space 3} .9961609
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .6214469{col 31}{space 2} .3703837{col 42}{space 1}    1.68{col 51}{space 3}0.093{col 59}{space 4}-.1044918{col 72}{space 3} 1.347386
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.3916533{col 31}{space 2} .0631293{col 42}{space 1}   -6.20{col 51}{space 3}0.000{col 59}{space 4}-.5153845{col 72}{space 3}-.2679221
{txt}{space 14}age {c |}{col 19}{res}{space 2} -.069363{col 31}{space 2}  .009065{col 42}{space 1}   -7.65{col 51}{space 3}0.000{col 59}{space 4}  -.08713{col 72}{space 3} -.051596
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005824{col 31}{space 2} .0000885{col 42}{space 1}    6.58{col 51}{space 3}0.000{col 59}{space 4} .0004088{col 72}{space 3} .0007559
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} -.343497{col 31}{space 2} .0661803{col 42}{space 1}   -5.19{col 51}{space 3}0.000{col 59}{space 4}-.4732081{col 72}{space 3}-.2137859
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.437398{col 31}{space 2} .2294726{col 42}{space 1}    6.26{col 51}{space 3}0.000{col 59}{space 4} .9876396{col 72}{space 3} 1.887156
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1)  at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,978
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1226056{col 26}{space 2} .0639973{col 37}{space 1}    1.92{col 46}{space 3}0.055{col 54}{space 4}-.0028268{col 67}{space 3}  .248038
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0964454{col 26}{space 2} .0533324{col 37}{space 1}    1.81{col 46}{space 3}0.071{col 54}{space 4}-.0080843{col 67}{space 3}  .200975
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1043025{col 26}{space 2} .0491178{col 37}{space 1}    2.12{col 46}{space 3}0.034{col 54}{space 4} .0080335{col 67}{space 3} .2005715
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1852984{col 26}{space 2} .0526518{col 37}{space 1}    3.52{col 46}{space 3}0.000{col 54}{space 4} .0821029{col 67}{space 3}  .288494
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2726491{col 26}{space 2} .0581189{col 37}{space 1}    4.69{col 46}{space 3}0.000{col 54}{space 4} .1587382{col 67}{space 3}   .38656
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("France", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") xtitle("") saving(franceline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file franceline.gph saved)

{com}. 
. *Germany
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu  if ccode ==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2846.8328}  
Iteration 1:{space 3}log likelihood = {res:-2729.2372}  
Iteration 2:{space 3}log likelihood = {res:-2727.4391}  
Iteration 3:{space 3}log likelihood = {res:-2727.4379}  
Iteration 4:{space 3}log likelihood = {res:-2727.4379}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,802
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    238.79
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2727.4379{txt}{col 49}Pseudo R2{col 67}= {res}    0.0419

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .4792311{col 31}{space 2} .3252968{col 42}{space 1}    1.47{col 51}{space 3}0.141{col 59}{space 4}-.1583388{col 72}{space 3} 1.116801
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.0683836{col 31}{space 2} .1028438{col 42}{space 1}   -0.66{col 51}{space 3}0.506{col 59}{space 4}-.2699538{col 72}{space 3} .1331867
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.7205554{col 31}{space 2} .1126692{col 42}{space 1}   -6.40{col 51}{space 3}0.000{col 59}{space 4} -.941383{col 72}{space 3}-.4997278
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.4486365{col 31}{space 2} .1073144{col 42}{space 1}   -4.18{col 51}{space 3}0.000{col 59}{space 4}-.6589688{col 72}{space 3}-.2383042
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.2088302{col 31}{space 2} .1045615{col 42}{space 1}   -2.00{col 51}{space 3}0.046{col 59}{space 4}-.4137669{col 72}{space 3}-.0038935
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.0845691{col 31}{space 2} .4707738{col 42}{space 1}   -0.18{col 51}{space 3}0.857{col 59}{space 4}-1.007269{col 72}{space 3} .8381305
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .4505122{col 31}{space 2} .4192124{col 42}{space 1}    1.07{col 51}{space 3}0.283{col 59}{space 4}-.3711291{col 72}{space 3} 1.272153
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .9794297{col 31}{space 2} .4177922{col 42}{space 1}    2.34{col 51}{space 3}0.019{col 59}{space 4}  .160572{col 72}{space 3} 1.798287
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}-.1923841{col 31}{space 2} .4195745{col 42}{space 1}   -0.46{col 51}{space 3}0.647{col 59}{space 4}-1.014735{col 72}{space 3} .6299668
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.6705885{col 31}{space 2} .0673927{col 42}{space 1}   -9.95{col 51}{space 3}0.000{col 59}{space 4}-.8026757{col 72}{space 3}-.5385013
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0521879{col 31}{space 2} .0101505{col 42}{space 1}   -5.14{col 51}{space 3}0.000{col 59}{space 4}-.0720825{col 72}{space 3}-.0322933
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005213{col 31}{space 2}  .000099{col 42}{space 1}    5.27{col 51}{space 3}0.000{col 59}{space 4} .0003273{col 72}{space 3} .0007153
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0057898{col 31}{space 2} .0677422{col 42}{space 1}    0.09{col 51}{space 3}0.932{col 59}{space 4}-.1269826{col 72}{space 3} .1385621
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .7207566{col 31}{space 2} .2540361{col 42}{space 1}    2.84{col 51}{space 3}0.005{col 59}{space 4} .2228551{col 72}{space 3} 1.218658
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1)  at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,802
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1085043{col 26}{space 2} .0767342{col 37}{space 1}    1.41{col 46}{space 3}0.157{col 54}{space 4}-.0418919{col 67}{space 3} .2589005
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .087021{col 26}{space 2} .0784267{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0666925{col 67}{space 3} .2407345
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1781375{col 26}{space 2} .0579941{col 37}{space 1}    3.07{col 46}{space 3}0.002{col 54}{space 4} .0644712{col 67}{space 3} .2918038
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3233917{col 26}{space 2} .0610545{col 37}{space 1}    5.30{col 46}{space 3}0.000{col 54}{space 4}  .203727{col 67}{space 3} .4430563
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0595686{col 26}{space 2} .0572656{col 37}{space 1}    1.04{col 46}{space 3}0.298{col 54}{space 4}  -.05267{col 67}{space 3} .1718072
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Germany", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") xtitle("") saving(germanyline, replace) yscale(range(-.1 0.4))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file germanyline.gph saved)

{com}. 
. *Italy
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu  if ccode ==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2828.0253}  
Iteration 1:{space 3}log likelihood = {res:-2765.9931}  
Iteration 2:{space 3}log likelihood = {res:-2765.4747}  
Iteration 3:{space 3}log likelihood = {res:-2765.4742}  
Iteration 4:{space 3}log likelihood = {res:-2765.4742}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,681
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    125.10
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2765.4742{txt}{col 49}Pseudo R2{col 67}= {res}    0.0221

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} -.784342{col 31}{space 2} .5430626{col 42}{space 1}   -1.44{col 51}{space 3}0.149{col 59}{space 4}-1.848725{col 72}{space 3} .2800412
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .4953104{col 31}{space 2} .1004309{col 42}{space 1}    4.93{col 51}{space 3}0.000{col 59}{space 4} .2984695{col 72}{space 3} .6921513
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.2452439{col 31}{space 2} .1153378{col 42}{space 1}   -2.13{col 51}{space 3}0.033{col 59}{space 4}-.4713019{col 72}{space 3}-.0191859
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}  .085253{col 31}{space 2} .1108525{col 42}{space 1}    0.77{col 51}{space 3}0.442{col 59}{space 4}-.1320139{col 72}{space 3} .3025199
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .6381002{col 31}{space 2} .1088526{col 42}{space 1}    5.86{col 51}{space 3}0.000{col 59}{space 4} .4247531{col 72}{space 3} .8514473
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} 1.120981{col 31}{space 2} .7035113{col 42}{space 1}    1.59{col 51}{space 3}0.111{col 59}{space 4}-.2578757{col 72}{space 3} 2.499838
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.523363{col 31}{space 2} .6640351{col 42}{space 1}    2.29{col 51}{space 3}0.022{col 59}{space 4} .2218783{col 72}{space 3} 2.824848
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.291839{col 31}{space 2} .6245787{col 42}{space 1}    2.07{col 51}{space 3}0.039{col 59}{space 4}  .067687{col 72}{space 3}  2.51599
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .7725306{col 31}{space 2} .6211774{col 42}{space 1}    1.24{col 51}{space 3}0.214{col 59}{space 4}-.4449547{col 72}{space 3} 1.990016
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.1220961{col 31}{space 2} .0653589{col 42}{space 1}   -1.87{col 51}{space 3}0.062{col 59}{space 4}-.2501972{col 72}{space 3} .0060051
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0124937{col 31}{space 2} .0109694{col 42}{space 1}   -1.14{col 51}{space 3}0.255{col 59}{space 4}-.0339934{col 72}{space 3} .0090059
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0000564{col 31}{space 2} .0001104{col 42}{space 1}    0.51{col 51}{space 3}0.609{col 59}{space 4}  -.00016{col 72}{space 3} .0002728
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .1164204{col 31}{space 2}  .074294{col 42}{space 1}    1.57{col 51}{space 3}0.117{col 59}{space 4}-.0291932{col 72}{space 3}  .262034
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.6263182{col 31}{space 2} .2684915{col 42}{space 1}   -2.33{col 51}{space 3}0.020{col 59}{space 4}-1.152552{col 72}{space 3}-.1000844
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1)  at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,681
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1178393{col 26}{space 2} .0633262{col 37}{space 1}   -1.86{col 46}{space 3}0.063{col 54}{space 4}-.2419563{col 67}{space 3} .0062778
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0796868{col 26}{space 2} .1091076{col 37}{space 1}    0.73{col 46}{space 3}0.465{col 54}{space 4}-.1341602{col 67}{space 3} .2935338
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1458259{col 26}{space 2} .0859307{col 37}{space 1}    1.70{col 46}{space 3}0.090{col 54}{space 4}-.0225952{col 67}{space 3}  .314247
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1093347{col 26}{space 2} .0714197{col 37}{space 1}    1.53{col 46}{space 3}0.126{col 54}{space 4}-.0306453{col 67}{space 3} .2493148
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0027822{col 26}{space 2} .0709644{col 37}{space 1}   -0.04{col 46}{space 3}0.969{col 54}{space 4}-.1418699{col 67}{space 3} .1363054
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Italy", margin(0 0 5 0) size(medium)  position(12) span)  scheme(538bw) ///
> ytitle("") xtitle("") saving(italyline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file italyline.gph saved)

{com}.  
. gr combine ukline.gph franceline.gph germanyline.gph italyline.gph, ycommon iscale(0.8) /// 
> l1("Marginal Effect", size(small)) title(Favorabilty Toward Russia)
{res}{txt}
{com}. graph export "russia_dydx2", as(eps) replace 
{txt}(file russia_dydx2 written in EPS format)

{com}. 
. 
. ***************************************************
. *Favorability toward United States - Dem. Controls*
. ***************************************************
. 
. *United Kingdom
. logit SupportUSBi i.pr1##i.year female age agesq i.unedu if ccode ==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3562.6679}  
Iteration 1:{space 3}log likelihood = {res:-3503.4572}  
Iteration 2:{space 3}log likelihood = {res:-3503.2105}  
Iteration 3:{space 3}log likelihood = {res:-3503.2104}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,614
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    118.91
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3503.2104{txt}{col 49}Pseudo R2{col 67}= {res}    0.0167

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      SupportUSBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .0319972{col 31}{space 2} .3269557{col 42}{space 1}    0.10{col 51}{space 3}0.922{col 59}{space 4}-.6088242{col 72}{space 3} .6728187
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0329389{col 31}{space 2} .1058448{col 42}{space 1}    0.31{col 51}{space 3}0.756{col 59}{space 4} -.174513{col 72}{space 3} .2403909
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} .2519108{col 31}{space 2}  .106458{col 42}{space 1}    2.37{col 51}{space 3}0.018{col 59}{space 4} .0432569{col 72}{space 3} .4605647
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .3427494{col 31}{space 2} .1097457{col 42}{space 1}    3.12{col 51}{space 3}0.002{col 59}{space 4} .1276516{col 72}{space 3} .5578471
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .2899091{col 31}{space 2} .0997841{col 42}{space 1}    2.91{col 51}{space 3}0.004{col 59}{space 4} .0943359{col 72}{space 3} .4854823
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.4788857{col 31}{space 2} .0996486{col 42}{space 1}   -4.81{col 51}{space 3}0.000{col 59}{space 4}-.6741934{col 72}{space 3} -.283578
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .0295981{col 31}{space 2} .4072882{col 42}{space 1}    0.07{col 51}{space 3}0.942{col 59}{space 4}-.7686722{col 72}{space 3} .8278683
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .1973582{col 31}{space 2} .4199258{col 42}{space 1}    0.47{col 51}{space 3}0.638{col 59}{space 4}-.6256812{col 72}{space 3} 1.020398
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .1357126{col 31}{space 2} .4415784{col 42}{space 1}    0.31{col 51}{space 3}0.759{col 59}{space 4}-.7297652{col 72}{space 3}  1.00119
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}-.4962018{col 31}{space 2} .3778848{col 42}{space 1}   -1.31{col 51}{space 3}0.189{col 59}{space 4}-1.236842{col 72}{space 3} .2444388
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .3171339{col 31}{space 2} .5344384{col 42}{space 1}    0.59{col 51}{space 3}0.553{col 59}{space 4}-.7303461{col 72}{space 3} 1.364614
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.1825647{col 31}{space 2} .0577522{col 42}{space 1}   -3.16{col 51}{space 3}0.002{col 59}{space 4} -.295757{col 72}{space 3}-.0693724
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0197748{col 31}{space 2} .0087925{col 42}{space 1}   -2.25{col 51}{space 3}0.025{col 59}{space 4}-.0370077{col 72}{space 3}-.0025419
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0001861{col 31}{space 2} .0000824{col 42}{space 1}    2.26{col 51}{space 3}0.024{col 59}{space 4} .0000246{col 72}{space 3} .0003476
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1475301{col 31}{space 2} .0620345{col 42}{space 1}   -2.38{col 51}{space 3}0.017{col 59}{space 4}-.2691155{col 72}{space 3}-.0259448
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.287188{col 31}{space 2} .2356785{col 42}{space 1}    5.46{col 51}{space 3}0.000{col 59}{space 4} .8252671{col 72}{space 3}  1.74911
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012/2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     5,614
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportUSBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0071615{col 26}{space 2} .0728438{col 37}{space 1}    0.10{col 46}{space 3}0.922{col 54}{space 4}-.1356096{col 67}{space 3} .1499327
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .013573{col 26}{space 2} .0531726{col 37}{space 1}    0.26{col 46}{space 3}0.799{col 54}{space 4}-.0906433{col 67}{space 3} .1177893
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0447577{col 26}{space 2} .0492412{col 37}{space 1}    0.91{col 46}{space 3}0.363{col 54}{space 4}-.0517533{col 67}{space 3} .1412687
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0318184{col 26}{space 2} .0543991{col 37}{space 1}    0.58{col 46}{space 3}0.559{col 54}{space 4}-.0748018{col 67}{space 3} .1384386
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1021983{col 26}{space 2} .0439735{col 37}{space 1}   -2.32{col 46}{space 3}0.020{col 54}{space 4}-.1883848{col 67}{space 3}-.0160118
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0842387{col 26}{space 2} .0987733{col 37}{space 1}    0.85{col 46}{space 3}0.394{col 54}{space 4}-.1093534{col 67}{space 3} .2778307
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Great Britain", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") xtitle("") saving(ukline, replace) yscale(range(-.1 0.3))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file ukline.gph saved)

{com}.  
. *France
. logit SupportUSBi i.pr1##i.year female age agesq i.unedu if ccode ==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3813.1253}  
Iteration 1:{space 3}log likelihood = {res:-3670.8238}  
Iteration 2:{space 3}log likelihood = {res:-3670.0574}  
Iteration 3:{space 3}log likelihood = {res:-3670.0573}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,906
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    286.14
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3670.0573{txt}{col 49}Pseudo R2{col 67}= {res}    0.0375

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      SupportUSBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .1330205{col 31}{space 2} .2944145{col 42}{space 1}    0.45{col 51}{space 3}0.651{col 59}{space 4}-.4440214{col 72}{space 3} .7100624
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.2081221{col 31}{space 2} .0996802{col 42}{space 1}   -2.09{col 51}{space 3}0.037{col 59}{space 4}-.4034916{col 72}{space 3}-.0127526
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} .2097351{col 31}{space 2} .1037285{col 42}{space 1}    2.02{col 51}{space 3}0.043{col 59}{space 4}  .006431{col 72}{space 3} .4130391
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .1315911{col 31}{space 2} .1030842{col 42}{space 1}    1.28{col 51}{space 3}0.202{col 59}{space 4}-.0704502{col 72}{space 3} .3336325
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}-.0379321{col 31}{space 2} .1025197{col 42}{space 1}   -0.37{col 51}{space 3}0.711{col 59}{space 4} -.238867{col 72}{space 3} .1630027
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-1.115926{col 31}{space 2} .0982852{col 42}{space 1}  -11.35{col 51}{space 3}0.000{col 59}{space 4}-1.308562{col 72}{space 3}-.9232908
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.2665079{col 31}{space 2} .3728709{col 42}{space 1}   -0.71{col 51}{space 3}0.475{col 59}{space 4}-.9973214{col 72}{space 3} .4643057
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} -.232099{col 31}{space 2} .3842902{col 42}{space 1}   -0.60{col 51}{space 3}0.546{col 59}{space 4}-.9852939{col 72}{space 3} .5210959
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}-.3943312{col 31}{space 2} .3748346{col 42}{space 1}   -1.05{col 51}{space 3}0.293{col 59}{space 4}-1.128994{col 72}{space 3} .3403312
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .1173752{col 31}{space 2} .3890027{col 42}{space 1}    0.30{col 51}{space 3}0.763{col 59}{space 4}-.6450561{col 72}{space 3} .8798065
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} 1.016147{col 31}{space 2}  .396515{col 42}{space 1}    2.56{col 51}{space 3}0.010{col 59}{space 4} .2389921{col 72}{space 3} 1.793302
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}  .039608{col 31}{space 2} .0562876{col 42}{space 1}    0.70{col 51}{space 3}0.482{col 59}{space 4}-.0707136{col 72}{space 3} .1499295
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0372525{col 31}{space 2} .0086223{col 42}{space 1}   -4.32{col 51}{space 3}0.000{col 59}{space 4}-.0541519{col 72}{space 3}-.0203531
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0003333{col 31}{space 2} .0000837{col 42}{space 1}    3.98{col 51}{space 3}0.000{col 59}{space 4} .0001693{col 72}{space 3} .0004973
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0178882{col 31}{space 2} .0583536{col 42}{space 1}   -0.31{col 51}{space 3}0.759{col 59}{space 4}-.1322592{col 72}{space 3} .0964829
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.718511{col 31}{space 2} .2220393{col 42}{space 1}    7.74{col 51}{space 3}0.000{col 59}{space 4} 1.283322{col 72}{space 3}   2.1537
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012/2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     5,906
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportUSBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  .027543{col 26}{space 2} .0594881{col 37}{space 1}    0.46{col 46}{space 3}0.643{col 54}{space 4}-.0890516{col 67}{space 3} .1441376
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0309781{col 26}{space 2} .0541057{col 37}{space 1}   -0.57{col 46}{space 3}0.567{col 54}{space 4}-.1370233{col 67}{space 3} .0750671
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0197127{col 26}{space 2}  .050251{col 37}{space 1}   -0.39{col 46}{space 3}0.695{col 54}{space 4}-.1182028{col 67}{space 3} .0787775
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0554893{col 26}{space 2} .0514141{col 37}{space 1}   -1.08{col 46}{space 3}0.280{col 54}{space 4}-.1562591{col 67}{space 3} .0452804
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0513826{col 26}{space 2} .0500648{col 37}{space 1}    1.03{col 46}{space 3}0.305{col 54}{space 4}-.0467426{col 67}{space 3} .1495077
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .2739949{col 26}{space 2} .0564515{col 37}{space 1}    4.85{col 46}{space 3}0.000{col 54}{space 4}  .163352{col 67}{space 3} .3846377
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("France", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") xtitle("") saving(franceline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file franceline.gph saved)

{com}. 
. *Germany
. logit SupportUSBi i.pr1##i.year female age agesq i.unedu if ccode ==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3990.5744}  
Iteration 1:{space 3}log likelihood = {res:-3881.5409}  
Iteration 2:{space 3}log likelihood = {res:-3881.0723}  
Iteration 3:{space 3}log likelihood = {res:-3881.0707}  
Iteration 4:{space 3}log likelihood = {res:-3881.0707}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,760
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    219.01
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3881.0707{txt}{col 49}Pseudo R2{col 67}= {res}    0.0274

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      SupportUSBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-1.064485{col 31}{space 2} .3372419{col 42}{space 1}   -3.16{col 51}{space 3}0.002{col 59}{space 4}-1.725467{col 72}{space 3}-.4035031
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0528761{col 31}{space 2} .0945321{col 42}{space 1}    0.56{col 51}{space 3}0.576{col 59}{space 4}-.1324034{col 72}{space 3} .2381555
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.0891718{col 31}{space 2} .0942971{col 42}{space 1}   -0.95{col 51}{space 3}0.344{col 59}{space 4}-.2739907{col 72}{space 3} .0956471
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .0015542{col 31}{space 2} .0948475{col 42}{space 1}    0.02{col 51}{space 3}0.987{col 59}{space 4}-.1843435{col 72}{space 3}  .187452
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .2197042{col 31}{space 2} .0964418{col 42}{space 1}    2.28{col 51}{space 3}0.023{col 59}{space 4} .0306818{col 72}{space 3} .4087265
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.7347127{col 31}{space 2}  .096028{col 42}{space 1}   -7.65{col 51}{space 3}0.000{col 59}{space 4}-.9229242{col 72}{space 3}-.5465013
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .1136245{col 31}{space 2} .4833379{col 42}{space 1}    0.24{col 51}{space 3}0.814{col 59}{space 4}-.8337005{col 72}{space 3} 1.060949
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2}-.1518395{col 31}{space 2} .4386612{col 42}{space 1}   -0.35{col 51}{space 3}0.729{col 59}{space 4}  -1.0116{col 72}{space 3} .7079207
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}-.6992308{col 31}{space 2} .4686828{col 42}{space 1}   -1.49{col 51}{space 3}0.136{col 59}{space 4}-1.617832{col 72}{space 3} .2193706
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .1640304{col 31}{space 2} .4320968{col 42}{space 1}    0.38{col 51}{space 3}0.704{col 59}{space 4}-.6828638{col 72}{space 3} 1.010925
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .5703487{col 31}{space 2} .4365344{col 42}{space 1}    1.31{col 51}{space 3}0.191{col 59}{space 4} -.285243{col 72}{space 3}  1.42594
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.0194675{col 31}{space 2} .0541256{col 42}{space 1}   -0.36{col 51}{space 3}0.719{col 59}{space 4}-.1255518{col 72}{space 3} .0866168
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0233561{col 31}{space 2} .0086999{col 42}{space 1}   -2.68{col 51}{space 3}0.007{col 59}{space 4}-.0404075{col 72}{space 3}-.0063047
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0002259{col 31}{space 2} .0000846{col 42}{space 1}    2.67{col 51}{space 3}0.008{col 59}{space 4} .0000601{col 72}{space 3} .0003917
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0060478{col 31}{space 2} .0550193{col 42}{space 1}    0.11{col 51}{space 3}0.912{col 59}{space 4}-.1017879{col 72}{space 3} .1138836
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .7492903{col 31}{space 2} .2209302{col 42}{space 1}    3.39{col 51}{space 3}0.001{col 59}{space 4} .3162751{col 72}{space 3} 1.182306
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012/2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     5,760
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportUSBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2533579{col 26}{space 2} .0709967{col 37}{space 1}   -3.57{col 46}{space 3}0.000{col 54}{space 4} -.392509{col 67}{space 3}-.1142069
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2304765{col 26}{space 2} .0772214{col 37}{space 1}   -2.98{col 46}{space 3}0.003{col 54}{space 4}-.3818275{col 67}{space 3}-.0791254
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2790382{col 26}{space 2} .0537491{col 37}{space 1}   -5.19{col 46}{space 3}0.000{col 54}{space 4}-.3843845{col 67}{space 3} -.173692
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.3771729{col 26}{space 2} .0487729{col 37}{space 1}   -7.73{col 46}{space 3}0.000{col 54}{space 4} -.472766{col 67}{space 3}-.2815798
{txt}{space 10}5  {c |}{col 14}{res}{space 2} -.221066{col 26}{space 2} .0638489{col 37}{space 1}   -3.46{col 46}{space 3}0.001{col 54}{space 4}-.3462076{col 67}{space 3}-.0959244
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.1062559{col 26}{space 2} .0546077{col 37}{space 1}   -1.95{col 46}{space 3}0.052{col 54}{space 4} -.213285{col 67}{space 3} .0007733
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Germany", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") xtitle("") saving(germanyline, replace) yscale(range(-.1 0.4))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file germanyline.gph saved)

{com}. 
. *Italy
. logit SupportUSBi i.pr1##i.year female age agesq i.unedu if ccode ==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3061.8686}  
Iteration 1:{space 3}log likelihood = {res:-2968.5147}  
Iteration 2:{space 3}log likelihood = {res:-2965.4169}  
Iteration 3:{space 3}log likelihood = {res:-2965.3891}  
Iteration 4:{space 3}log likelihood = {res: -2965.389}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,803
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    192.96
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -2965.389{txt}{col 49}Pseudo R2{col 67}= {res}    0.0315

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      SupportUSBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.5570918{col 31}{space 2} .3816355{col 42}{space 1}   -1.46{col 51}{space 3}0.144{col 59}{space 4}-1.305084{col 72}{space 3}    .1909
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .3998245{col 31}{space 2} .1136772{col 42}{space 1}    3.52{col 51}{space 3}0.000{col 59}{space 4} .1770213{col 72}{space 3} .6226277
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} .2286941{col 31}{space 2} .1166989{col 42}{space 1}    1.96{col 51}{space 3}0.050{col 59}{space 4}-.0000315{col 72}{space 3} .4574196
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .5226055{col 31}{space 2} .1223994{col 42}{space 1}    4.27{col 51}{space 3}0.000{col 59}{space 4} .2827072{col 72}{space 3} .7625039
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} -.039562{col 31}{space 2} .1096206{col 42}{space 1}   -0.36{col 51}{space 3}0.718{col 59}{space 4}-.2544145{col 72}{space 3} .1752905
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.6358716{col 31}{space 2} .1089111{col 42}{space 1}   -5.84{col 51}{space 3}0.000{col 59}{space 4}-.8493335{col 72}{space 3}-.4224096
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}  .402875{col 31}{space 2} .6835492{col 42}{space 1}    0.59{col 51}{space 3}0.556{col 59}{space 4}-.9368568{col 72}{space 3} 1.742607
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.914878{col 31}{space 2} .8288663{col 42}{space 1}    2.31{col 51}{space 3}0.021{col 59}{space 4} .2903297{col 72}{space 3} 3.539426
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.647589{col 31}{space 2} .7139053{col 42}{space 1}    2.31{col 51}{space 3}0.021{col 59}{space 4} .2483604{col 72}{space 3} 3.046818
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .2454069{col 31}{space 2} .4740019{col 42}{space 1}    0.52{col 51}{space 3}0.605{col 59}{space 4}-.6836198{col 72}{space 3} 1.174434
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .6811217{col 31}{space 2} .4857988{col 42}{space 1}    1.40{col 51}{space 3}0.161{col 59}{space 4}-.2710265{col 72}{space 3}  1.63327
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2} .2768176{col 31}{space 2} .0646177{col 42}{space 1}    4.28{col 51}{space 3}0.000{col 59}{space 4} .1501692{col 72}{space 3} .4034659
{txt}{space 14}age {c |}{col 19}{res}{space 2} -.020957{col 31}{space 2} .0109775{col 42}{space 1}   -1.91{col 51}{space 3}0.056{col 59}{space 4}-.0424726{col 72}{space 3} .0005585
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0001464{col 31}{space 2} .0001079{col 42}{space 1}    1.36{col 51}{space 3}0.175{col 59}{space 4}-.0000651{col 72}{space 3} .0003579
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}  .058177{col 31}{space 2} .0735955{col 42}{space 1}    0.79{col 51}{space 3}0.429{col 59}{space 4}-.0860676{col 72}{space 3} .2024216
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.670804{col 31}{space 2} .2767579{col 42}{space 1}    6.04{col 51}{space 3}0.000{col 59}{space 4} 1.128369{col 72}{space 3}  2.21324
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012/2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     5,803
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportUSBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1124025{col 26}{space 2} .0848751{col 37}{space 1}   -1.32{col 46}{space 3}0.185{col 54}{space 4}-.2787546{col 67}{space 3} .0539496
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0226284{col 26}{space 2}  .087155{col 37}{space 1}   -0.26{col 46}{space 3}0.795{col 54}{space 4} -.193449{col 67}{space 3} .1481922
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .135417{col 26}{space 2} .0422623{col 37}{space 1}    3.20{col 46}{space 3}0.001{col 54}{space 4} .0525844{col 67}{space 3} .2182496
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0951633{col 26}{space 2} .0341237{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} .0282821{col 67}{space 3} .1620445
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0607548{col 26}{space 2} .0580483{col 37}{space 1}   -1.05{col 46}{space 3}0.295{col 54}{space 4}-.1745273{col 67}{space 3} .0530177
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0279286{col 26}{space 2} .0665498{col 37}{space 1}    0.42{col 46}{space 3}0.675{col 54}{space 4}-.1025067{col 67}{space 3} .1583638
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Italy", margin(0 0 5 0) size(medium)  position(12) span)  scheme(538bw) ///
> ytitle("") xtitle("") saving(italyline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file italyline.gph saved)

{com}. 
. gr combine ukline.gph franceline.gph germanyline.gph italyline.gph, ycommon iscale(0.8) /// 
> l1("Marginal Effect", size(small)) title(Favorabilty Toward United States)
{res}{txt}
{com}. graph export "us_dydx2", as(eps) replace 
{txt}(file us_dydx2 written in EPS format)

{com}.  
. 
. ******************************************
. *Favorability toward NATO - Dem. Controls*
. ******************************************
. 
. *United Kingdom
. logit SupportNatoBi i.pr1##i.year female age agesq i.unedu if ccode ==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1675.0423}  
Iteration 1:{space 3}log likelihood = {res:-1662.0257}  
Iteration 2:{space 3}log likelihood = {res:  -1661.71}  
Iteration 3:{space 3}log likelihood = {res:  -1661.71}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,146
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}     26.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0052
{txt}Log likelihood = {res}  -1661.71{txt}{col 49}Pseudo R2{col 67}= {res}    0.0080

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    SupportNatoBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.4355938{col 31}{space 2} .3465319{col 42}{space 1}   -1.26{col 51}{space 3}0.209{col 59}{space 4}-1.114784{col 72}{space 3} .2435962
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0020388{col 31}{space 2} .1284388{col 42}{space 1}    0.02{col 51}{space 3}0.987{col 59}{space 4}-.2496967{col 72}{space 3} .2537743
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .0215209{col 31}{space 2} .1277561{col 42}{space 1}    0.17{col 51}{space 3}0.866{col 59}{space 4}-.2288766{col 72}{space 3} .2719183
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .1899894{col 31}{space 2} .1253635{col 42}{space 1}    1.52{col 51}{space 3}0.130{col 59}{space 4}-.0557186{col 72}{space 3} .4356974
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}  .213816{col 31}{space 2} .4362995{col 42}{space 1}    0.49{col 51}{space 3}0.624{col 59}{space 4}-.6413153{col 72}{space 3} 1.068947
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .8212767{col 31}{space 2} .4892789{col 42}{space 1}    1.68{col 51}{space 3}0.093{col 59}{space 4}-.1376923{col 72}{space 3} 1.780246
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}-.7306262{col 31}{space 2}  .521294{col 42}{space 1}   -1.40{col 51}{space 3}0.161{col 59}{space 4}-1.752344{col 72}{space 3} .2910913
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2} -.113464{col 31}{space 2} .0863524{col 42}{space 1}   -1.31{col 51}{space 3}0.189{col 59}{space 4}-.2827117{col 72}{space 3} .0557836
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0187904{col 31}{space 2} .0132353{col 42}{space 1}    1.42{col 51}{space 3}0.156{col 59}{space 4}-.0071504{col 72}{space 3} .0447312
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}-.0001491{col 31}{space 2} .0001256{col 42}{space 1}   -1.19{col 51}{space 3}0.235{col 59}{space 4}-.0003953{col 72}{space 3} .0000972
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .2894761{col 31}{space 2} .0917587{col 42}{space 1}    3.15{col 51}{space 3}0.002{col 59}{space 4} .1096324{col 72}{space 3} .4693198
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .5670174{col 31}{space 2} .3440199{col 42}{space 1}    1.65{col 51}{space 3}0.099{col 59}{space 4}-.1072493{col 72}{space 3} 1.241284
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2013 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     3,146
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportNatoBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0856463{col 26}{space 2} .0737777{col 37}{space 1}   -1.16{col 46}{space 3}0.246{col 54}{space 4}-.2302478{col 67}{space 3} .0589553
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0414692{col 26}{space 2} .0518203{col 37}{space 1}   -0.80{col 46}{space 3}0.424{col 54}{space 4}-.1430351{col 67}{space 3} .0600968
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0604046{col 26}{space 2} .0488875{col 37}{space 1}    1.24{col 46}{space 3}0.217{col 54}{space 4}-.0354131{col 67}{space 3} .1562223
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.2431861{col 26}{space 2}  .094278{col 37}{space 1}   -2.58{col 46}{space 3}0.010{col 54}{space 4}-.4279675{col 67}{space 3}-.0584046
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Great Britain", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") xtitle("") saving(ukline, replace) yscale(range(-.1 0.3))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file ukline.gph saved)

{com}.  
. *France
. logit SupportNatoBi i.pr1##i.year female age agesq i.unedu if ccode ==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2524.2724}  
Iteration 1:{space 3}log likelihood = {res:-2506.8281}  
Iteration 2:{space 3}log likelihood = {res: -2506.789}  
Iteration 3:{space 3}log likelihood = {res: -2506.789}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,904
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}     34.97
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res} -2506.789{txt}{col 49}Pseudo R2{col 67}= {res}    0.0069

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    SupportNatoBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2236292{col 31}{space 2} .2774377{col 42}{space 1}   -0.81{col 51}{space 3}0.420{col 59}{space 4} -.767397{col 72}{space 3} .3201386
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.2613833{col 31}{space 2} .0998285{col 42}{space 1}   -2.62{col 51}{space 3}0.009{col 59}{space 4}-.4570435{col 72}{space 3} -.065723
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} -.128415{col 31}{space 2} .1009584{col 42}{space 1}   -1.27{col 51}{space 3}0.203{col 59}{space 4}-.3262898{col 72}{space 3} .0694598
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.1750508{col 31}{space 2} .1011319{col 42}{space 1}   -1.73{col 51}{space 3}0.083{col 59}{space 4}-.3732657{col 72}{space 3} .0231641
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.3344945{col 31}{space 2} .3557189{col 42}{space 1}   -0.94{col 51}{space 3}0.347{col 59}{space 4}-1.031691{col 72}{space 3} .3627018
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}  .159858{col 31}{space 2} .3596159{col 42}{space 1}    0.44{col 51}{space 3}0.657{col 59}{space 4}-.5449761{col 72}{space 3} .8646921
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}  .116462{col 31}{space 2} .3808129{col 42}{space 1}    0.31{col 51}{space 3}0.760{col 59}{space 4}-.6299176{col 72}{space 3} .8628415
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}  .007892{col 31}{space 2} .0676877{col 42}{space 1}    0.12{col 51}{space 3}0.907{col 59}{space 4}-.1247735{col 72}{space 3} .1405574
{txt}{space 14}age {c |}{col 19}{res}{space 2} -.030014{col 31}{space 2} .0102726{col 42}{space 1}   -2.92{col 51}{space 3}0.003{col 59}{space 4} -.050148{col 72}{space 3}-.0098801
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0002718{col 31}{space 2} .0000999{col 42}{space 1}    2.72{col 51}{space 3}0.007{col 59}{space 4}  .000076{col 72}{space 3} .0004676
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}  .148335{col 31}{space 2} .0704747{col 42}{space 1}    2.10{col 51}{space 3}0.035{col 59}{space 4} .0102072{col 72}{space 3} .2864629
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.445422{col 31}{space 2} .2601813{col 42}{space 1}    5.56{col 51}{space 3}0.000{col 59}{space 4} .9354757{col 72}{space 3} 1.955368
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2013 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     3,904
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportNatoBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0497539{col 26}{space 2} .0635727{col 37}{space 1}   -0.78{col 46}{space 3}0.434{col 54}{space 4}-.1743542{col 67}{space 3} .0748463
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1359866{col 26}{space 2}  .055515{col 37}{space 1}   -2.45{col 46}{space 3}0.014{col 54}{space 4} -.244794{col 67}{space 3}-.0271791
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0144139{col 26}{space 2} .0524837{col 37}{space 1}   -0.27{col 46}{space 3}0.784{col 54}{space 4}-.1172801{col 67}{space 3} .0884522
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0246978{col 26}{space 2} .0610006{col 37}{space 1}   -0.40{col 46}{space 3}0.686{col 54}{space 4}-.1442567{col 67}{space 3} .0948611
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("France", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") xtitle("") saving(franceline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file franceline.gph saved)

{com}. 
. *Germany
. logit SupportNatoBi i.pr1##i.year female age agesq i.unedu if ccode ==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2350.3673}  
Iteration 1:{space 3}log likelihood = {res:-2292.8847}  
Iteration 2:{space 3}log likelihood = {res:-2292.5639}  
Iteration 3:{space 3}log likelihood = {res:-2292.5637}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,728
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}    115.61
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2292.5637{txt}{col 49}Pseudo R2{col 67}= {res}    0.0246

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    SupportNatoBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} -1.35891{col 31}{space 2} .3220339{col 42}{space 1}   -4.22{col 51}{space 3}0.000{col 59}{space 4}-1.990085{col 72}{space 3}-.7277353
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0046552{col 31}{space 2} .1034414{col 42}{space 1}    0.05{col 51}{space 3}0.964{col 59}{space 4}-.1980862{col 72}{space 3} .2073966
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.1597494{col 31}{space 2} .1020976{col 42}{space 1}   -1.56{col 51}{space 3}0.118{col 59}{space 4}-.3598571{col 72}{space 3} .0403583
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .2396611{col 31}{space 2} .1063876{col 42}{space 1}    2.25{col 51}{space 3}0.024{col 59}{space 4} .0311453{col 72}{space 3}  .448177
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}  .151413{col 31}{space 2} .4705195{col 42}{space 1}    0.32{col 51}{space 3}0.748{col 59}{space 4}-.7707883{col 72}{space 3} 1.073614
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}-.3388613{col 31}{space 2}  .434455{col 42}{space 1}   -0.78{col 51}{space 3}0.435{col 59}{space 4}-1.190377{col 72}{space 3} .5126548
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .1828757{col 31}{space 2} .4111431{col 42}{space 1}    0.44{col 51}{space 3}0.656{col 59}{space 4}  -.62295{col 72}{space 3} .9887014
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.1498807{col 31}{space 2} .0716405{col 42}{space 1}   -2.09{col 51}{space 3}0.036{col 59}{space 4}-.2902935{col 72}{space 3}-.0094679
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0019663{col 31}{space 2} .0115916{col 42}{space 1}   -0.17{col 51}{space 3}0.865{col 59}{space 4}-.0246855{col 72}{space 3} .0207528
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}-.0000118{col 31}{space 2} .0001126{col 42}{space 1}   -0.11{col 51}{space 3}0.916{col 59}{space 4}-.0002326{col 72}{space 3} .0002089
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0743581{col 31}{space 2} .0734749{col 42}{space 1}    1.01{col 51}{space 3}0.312{col 59}{space 4}-.0696501{col 72}{space 3} .2183662
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  .970944{col 31}{space 2} .2894604{col 42}{space 1}    3.35{col 51}{space 3}0.001{col 59}{space 4}  .403612{col 72}{space 3} 1.538276
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2013 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     3,728
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportNatoBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3253602{col 26}{space 2}  .074153{col 37}{space 1}   -4.39{col 46}{space 3}0.000{col 54}{space 4}-.4706975{col 67}{space 3}-.1800229
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2895834{col 26}{space 2}  .081839{col 37}{space 1}   -3.54{col 46}{space 3}0.000{col 54}{space 4}-.4499849{col 67}{space 3}-.1291819
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.3962411{col 26}{space 2} .0564985{col 37}{space 1}   -7.01{col 46}{space 3}0.000{col 54}{space 4}-.5069761{col 67}{space 3}-.2855062
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.2721051{col 26}{space 2} .0622401{col 37}{space 1}   -4.37{col 46}{space 3}0.000{col 54}{space 4}-.3940934{col 67}{space 3}-.1501167
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Germany", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") xtitle("") saving(germanyline, replace) yscale(range(-.1 0.4))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file germanyline.gph saved)

{com}. 
. *Italy
. logit SupportNatoBi i.pr1##i.year female age agesq i.unedu if ccode ==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2185.2171}  
Iteration 1:{space 3}log likelihood = {res:-2169.0079}  
Iteration 2:{space 3}log likelihood = {res:-2168.9518}  
Iteration 3:{space 3}log likelihood = {res:-2168.9518}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,559
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}     32.53
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0006
{txt}Log likelihood = {res}-2168.9518{txt}{col 49}Pseudo R2{col 67}= {res}    0.0074

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    SupportNatoBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2100509{col 31}{space 2} .3803566{col 42}{space 1}   -0.55{col 51}{space 3}0.581{col 59}{space 4}-.9555362{col 72}{space 3} .5354344
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0436309{col 31}{space 2}  .102621{col 42}{space 1}    0.43{col 51}{space 3}0.671{col 59}{space 4}-.1575025{col 72}{space 3} .2447643
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .0741053{col 31}{space 2} .1100944{col 42}{space 1}    0.67{col 51}{space 3}0.501{col 59}{space 4}-.1416757{col 72}{space 3} .2898863
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.2433666{col 31}{space 2} .1110995{col 42}{space 1}   -2.19{col 51}{space 3}0.028{col 59}{space 4}-.4611177{col 72}{space 3}-.0256156
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.1749148{col 31}{space 2} .5943811{col 42}{space 1}   -0.29{col 51}{space 3}0.769{col 59}{space 4} -1.33988{col 72}{space 3} .9900508
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .3740178{col 31}{space 2} .5153779{col 42}{space 1}    0.73{col 51}{space 3}0.468{col 59}{space 4}-.6361043{col 72}{space 3}  1.38414
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} 1.087346{col 31}{space 2}  .537555{col 42}{space 1}    2.02{col 51}{space 3}0.043{col 59}{space 4} .0337573{col 72}{space 3} 2.140934
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2} .2576795{col 31}{space 2} .0734272{col 42}{space 1}    3.51{col 51}{space 3}0.000{col 59}{space 4} .1137648{col 72}{space 3} .4015942
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0197587{col 31}{space 2}   .01257{col 42}{space 1}   -1.57{col 51}{space 3}0.116{col 59}{space 4}-.0443955{col 72}{space 3}  .004878
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}  .000234{col 31}{space 2} .0001268{col 42}{space 1}    1.85{col 51}{space 3}0.065{col 59}{space 4}-.0000144{col 72}{space 3} .0004825
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0971003{col 31}{space 2} .0828483{col 42}{space 1}    1.17{col 51}{space 3}0.241{col 59}{space 4}-.0652794{col 72}{space 3}   .25948
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.014034{col 31}{space 2} .3047851{col 42}{space 1}    3.33{col 51}{space 3}0.001{col 59}{space 4} .4166659{col 72}{space 3} 1.611402
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2013 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     3,559
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportNatoBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0456652{col 26}{space 2} .0854112{col 37}{space 1}   -0.53{col 46}{space 3}0.593{col 54}{space 4}-.2130681{col 67}{space 3} .1217377
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0848263{col 26}{space 2}   .10639{col 37}{space 1}   -0.80{col 46}{space 3}0.425{col 54}{space 4}-.2933469{col 67}{space 3} .1236943
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0320837{col 26}{space 2} .0657223{col 37}{space 1}    0.49{col 46}{space 3}0.625{col 54}{space 4}-.0967297{col 67}{space 3} .1608971
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1677182{col 26}{space 2} .0588833{col 37}{space 1}    2.85{col 46}{space 3}0.004{col 54}{space 4} .0523091{col 67}{space 3} .2831273
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) /// 
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> ciopts(fcolor(gs12)   lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> title("Italy", margin(0 0 5 0) size(medium)  position(12) span)  scheme(538bw) ///
> ytitle("") xtitle("") saving(italyline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file italyline.gph saved)

{com}.  
.  
. gr combine ukline.gph franceline.gph germanyline.gph italyline.gph, ycommon iscale(0.8) /// 
> l1("Marginal Effect", size(small)) title(Favorabilty Toward NATO)
{res}{txt}
{com}. graph export "nato_dydx2", as(eps) replace 
{txt}(file nato_dydx2 written in EPS format)

{com}. 
. ******************************************
. *Favorability toward China - Dem. Controls*
. ******************************************
. 
. *United Kingdom
. logit SupportChinaBi i.pr1##i.year female age agesq i.unedu if ccode ==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3580.7556}  
Iteration 1:{space 3}log likelihood = {res:-3502.4879}  
Iteration 2:{space 3}log likelihood = {res:-3502.3682}  
Iteration 3:{space 3}log likelihood = {res:-3502.3682}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,173
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    156.77
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3502.3682{txt}{col 49}Pseudo R2{col 67}= {res}    0.0219

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   SupportChinaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .0602201{col 31}{space 2} .3173956{col 42}{space 1}    0.19{col 51}{space 3}0.850{col 59}{space 4}-.5618638{col 72}{space 3}  .682304
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .1552572{col 31}{space 2} .1079746{col 42}{space 1}    1.44{col 51}{space 3}0.150{col 59}{space 4}-.0563692{col 72}{space 3} .3668836
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} -.151749{col 31}{space 2} .1048819{col 42}{space 1}   -1.45{col 51}{space 3}0.148{col 59}{space 4}-.3573137{col 72}{space 3} .0538157
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.1547923{col 31}{space 2} .1060864{col 42}{space 1}   -1.46{col 51}{space 3}0.145{col 59}{space 4}-.3627177{col 72}{space 3} .0531332
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}-.4407567{col 31}{space 2} .0977208{col 42}{space 1}   -4.51{col 51}{space 3}0.000{col 59}{space 4}-.6322859{col 72}{space 3}-.2492275
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.1807077{col 31}{space 2} .1023595{col 42}{space 1}   -1.77{col 51}{space 3}0.077{col 59}{space 4}-.3813287{col 72}{space 3} .0199133
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.5817005{col 31}{space 2}  .394489{col 42}{space 1}   -1.47{col 51}{space 3}0.140{col 59}{space 4}-1.354885{col 72}{space 3} .1914838
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2}-.1217596{col 31}{space 2} .3953227{col 42}{space 1}   -0.31{col 51}{space 3}0.758{col 59}{space 4}-.8965779{col 72}{space 3} .6530587
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .0628562{col 31}{space 2}  .415749{col 42}{space 1}    0.15{col 51}{space 3}0.880{col 59}{space 4}-.7519968{col 72}{space 3} .8777092
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}-.4203575{col 31}{space 2} .3695754{col 42}{space 1}   -1.14{col 51}{space 3}0.255{col 59}{space 4}-1.144712{col 72}{space 3}  .303997
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}-.3238955{col 31}{space 2} .5265147{col 42}{space 1}   -0.62{col 51}{space 3}0.538{col 59}{space 4}-1.355845{col 72}{space 3} .7080543
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.2932622{col 31}{space 2} .0568192{col 42}{space 1}   -5.16{col 51}{space 3}0.000{col 59}{space 4}-.4046257{col 72}{space 3}-.1818986
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0574745{col 31}{space 2} .0087092{col 42}{space 1}   -6.60{col 51}{space 3}0.000{col 59}{space 4}-.0745443{col 72}{space 3}-.0404047
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0004501{col 31}{space 2} .0000813{col 42}{space 1}    5.54{col 51}{space 3}0.000{col 59}{space 4} .0002908{col 72}{space 3} .0006094
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0904179{col 31}{space 2} .0610785{col 42}{space 1}   -1.48{col 51}{space 3}0.139{col 59}{space 4}-.2101296{col 72}{space 3} .0292938
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.095306{col 31}{space 2} .2359598{col 42}{space 1}    8.88{col 51}{space 3}0.000{col 59}{space 4} 1.632833{col 72}{space 3} 2.557779
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012/2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     5,173
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportChinaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0144778{col 26}{space 2} .0760205{col 37}{space 1}    0.19{col 46}{space 3}0.849{col 54}{space 4}-.1345197{col 67}{space 3} .1634752
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1264597{col 26}{space 2}  .057103{col 37}{space 1}   -2.21{col 46}{space 3}0.027{col 54}{space 4}-.2383794{col 67}{space 3}-.0145399
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0150613{col 26}{space 2} .0578107{col 37}{space 1}   -0.26{col 46}{space 3}0.794{col 54}{space 4}-.1283681{col 67}{space 3} .0982455
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .029968{col 26}{space 2} .0651379{col 37}{space 1}    0.46{col 46}{space 3}0.645{col 54}{space 4}-.0976999{col 67}{space 3} .1576358
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0853223{col 26}{space 2} .0436476{col 37}{space 1}   -1.95{col 46}{space 3}0.051{col 54}{space 4}-.1708699{col 67}{space 3} .0002254
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0644763{col 26}{space 2} .1021268{col 37}{space 1}   -0.63{col 46}{space 3}0.528{col 54}{space 4}-.2646412{col 67}{space 3} .1356887
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) ciopts(fcolor(gs12) lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> title("Great Britain", size(large) position(12)) scheme(538bw) ///
> ytitle("") saving(ukline, replace) yscale(range(-.1 0.3))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file ukline.gph saved)

{com}.  
. *France
. logit SupportChinaBi i.pr1##i.year female age agesq i.unedu if ccode ==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-4008.3368}  
Iteration 1:{space 3}log likelihood = {res:-3921.7917}  
Iteration 2:{space 3}log likelihood = {res:-3921.7317}  
Iteration 3:{space 3}log likelihood = {res:-3921.7317}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,881
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    173.21
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3921.7317{txt}{col 49}Pseudo R2{col 67}= {res}    0.0216

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   SupportChinaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2577578{col 31}{space 2} .2773738{col 42}{space 1}   -0.93{col 51}{space 3}0.353{col 59}{space 4}-.8014004{col 72}{space 3} .2858848
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .1242957{col 31}{space 2} .0961662{col 42}{space 1}    1.29{col 51}{space 3}0.196{col 59}{space 4}-.0641866{col 72}{space 3} .3127781
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} .2148541{col 31}{space 2} .0957653{col 42}{space 1}    2.24{col 51}{space 3}0.025{col 59}{space 4} .0271575{col 72}{space 3} .4025507
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .4690758{col 31}{space 2} .0957555{col 42}{space 1}    4.90{col 51}{space 3}0.000{col 59}{space 4} .2813985{col 72}{space 3} .6567531
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}-.1136277{col 31}{space 2} .0999583{col 42}{space 1}   -1.14{col 51}{space 3}0.256{col 59}{space 4}-.3095425{col 72}{space 3}  .082287
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .1358484{col 31}{space 2} .0969407{col 42}{space 1}    1.40{col 51}{space 3}0.161{col 59}{space 4}-.0541519{col 72}{space 3} .3258487
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .1520171{col 31}{space 2}  .358018{col 42}{space 1}    0.42{col 51}{space 3}0.671{col 59}{space 4}-.5496854{col 72}{space 3} .8537195
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .3648146{col 31}{space 2} .3557791{col 42}{space 1}    1.03{col 51}{space 3}0.305{col 59}{space 4}-.3324995{col 72}{space 3} 1.062129
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .1849591{col 31}{space 2} .3536405{col 42}{space 1}    0.52{col 51}{space 3}0.601{col 59}{space 4}-.5081636{col 72}{space 3} .8780818
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}  .225082{col 31}{space 2}  .362697{col 42}{space 1}    0.62{col 51}{space 3}0.535{col 59}{space 4} -.485791{col 72}{space 3}  .935955
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}  .898482{col 31}{space 2} .3730053{col 42}{space 1}    2.41{col 51}{space 3}0.016{col 59}{space 4} .1674051{col 72}{space 3} 1.629559
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.1665026{col 31}{space 2} .0537518{col 42}{space 1}   -3.10{col 51}{space 3}0.002{col 59}{space 4}-.2718542{col 72}{space 3}-.0611509
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0654718{col 31}{space 2} .0079468{col 42}{space 1}   -8.24{col 51}{space 3}0.000{col 59}{space 4}-.0810473{col 72}{space 3}-.0498963
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005526{col 31}{space 2} .0000772{col 42}{space 1}    7.16{col 51}{space 3}0.000{col 59}{space 4} .0004014{col 72}{space 3} .0007039
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1620612{col 31}{space 2} .0558789{col 42}{space 1}   -2.90{col 51}{space 3}0.004{col 59}{space 4}-.2715819{col 72}{space 3}-.0525405
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.413815{col 31}{space 2} .2042637{col 42}{space 1}    6.92{col 51}{space 3}0.000{col 59}{space 4} 1.013466{col 72}{space 3} 1.814165
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012/2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     5,881
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportChinaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0581887{col 26}{space 2} .0605619{col 37}{space 1}   -0.96{col 46}{space 3}0.337{col 54}{space 4}-.1768879{col 67}{space 3} .0605105
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0250103{col 26}{space 2} .0533425{col 37}{space 1}   -0.47{col 46}{space 3}0.639{col 54}{space 4}-.1295597{col 67}{space 3} .0795392
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0260207{col 26}{space 2} .0546374{col 37}{space 1}    0.48{col 46}{space 3}0.634{col 54}{space 4}-.0810666{col 67}{space 3} .1331081
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0178539{col 26}{space 2} .0540462{col 37}{space 1}   -0.33{col 46}{space 3}0.741{col 54}{space 4}-.1237825{col 67}{space 3} .0880746
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0073816{col 26}{space 2} .0528278{col 37}{space 1}   -0.14{col 46}{space 3}0.889{col 54}{space 4}-.1109221{col 67}{space 3} .0961589
{txt}{space 10}6  {c |}{col 14}{res}{space 2}   .15593{col 26}{space 2} .0598741{col 37}{space 1}    2.60{col 46}{space 3}0.009{col 54}{space 4}  .038579{col 67}{space 3} .2732811
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) ciopts(fcolor(gs12) lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> title("France", size(large) position(12)) scheme(538bw) ///
> ytitle("") saving(franceline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file franceline.gph saved)

{com}. 
.  *Germany
. logit SupportChinaBi i.pr1##i.year female age agesq i.unedu if ccode ==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3494.5266}  
Iteration 1:{space 3}log likelihood = {res:-3429.8864}  
Iteration 2:{space 3}log likelihood = {res:-3429.6605}  
Iteration 3:{space 3}log likelihood = {res:-3429.6605}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,529
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    129.73
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3429.6605{txt}{col 49}Pseudo R2{col 67}= {res}    0.0186

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   SupportChinaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .0480711{col 31}{space 2}  .335806{col 42}{space 1}    0.14{col 51}{space 3}0.886{col 59}{space 4}-.6100966{col 72}{space 3} .7062388
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.1748323{col 31}{space 2} .1041141{col 42}{space 1}   -1.68{col 51}{space 3}0.093{col 59}{space 4}-.3788922{col 72}{space 3} .0292275
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.0522718{col 31}{space 2} .1038233{col 42}{space 1}   -0.50{col 51}{space 3}0.615{col 59}{space 4}-.2557618{col 72}{space 3} .1512182
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .1643232{col 31}{space 2} .1018074{col 42}{space 1}    1.61{col 51}{space 3}0.107{col 59}{space 4}-.0352156{col 72}{space 3}  .363862
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}-.0888674{col 31}{space 2} .1053005{col 42}{space 1}   -0.84{col 51}{space 3}0.399{col 59}{space 4}-.2952525{col 72}{space 3} .1175177
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .2265539{col 31}{space 2} .1028329{col 42}{space 1}    2.20{col 51}{space 3}0.028{col 59}{space 4} .0250052{col 72}{space 3} .4281027
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .0854286{col 31}{space 2} .4911121{col 42}{space 1}    0.17{col 51}{space 3}0.862{col 59}{space 4}-.8771335{col 72}{space 3} 1.047991
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .4916643{col 31}{space 2} .4232144{col 42}{space 1}    1.16{col 51}{space 3}0.245{col 59}{space 4}-.3378208{col 72}{space 3} 1.321149
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .1810492{col 31}{space 2} .4253635{col 42}{space 1}    0.43{col 51}{space 3}0.670{col 59}{space 4}-.6526481{col 72}{space 3} 1.014746
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}  .731799{col 31}{space 2} .4343748{col 42}{space 1}    1.68{col 51}{space 3}0.092{col 59}{space 4}-.1195599{col 72}{space 3} 1.583158
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .0763649{col 31}{space 2} .4292713{col 42}{space 1}    0.18{col 51}{space 3}0.859{col 59}{space 4}-.7649915{col 72}{space 3} .9177212
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.3752801{col 31}{space 2}  .058751{col 42}{space 1}   -6.39{col 51}{space 3}0.000{col 59}{space 4}  -.49043{col 72}{space 3}-.2601302
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0475662{col 31}{space 2}  .009088{col 42}{space 1}   -5.23{col 51}{space 3}0.000{col 59}{space 4}-.0653784{col 72}{space 3} -.029754
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005203{col 31}{space 2} .0000881{col 42}{space 1}    5.90{col 51}{space 3}0.000{col 59}{space 4} .0003475{col 72}{space 3} .0006931
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0551523{col 31}{space 2} .0594129{col 42}{space 1}   -0.93{col 51}{space 3}0.353{col 59}{space 4}-.1715995{col 72}{space 3}  .061295
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .3563966{col 31}{space 2} .2310074{col 42}{space 1}    1.54{col 51}{space 3}0.123{col 59}{space 4}-.0963696{col 72}{space 3} .8091627
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012/2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     5,529
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportChinaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0103625{col 26}{space 2} .0729227{col 37}{space 1}    0.14{col 46}{space 3}0.887{col 54}{space 4}-.1325634{col 67}{space 3} .1532885
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0274079{col 26}{space 2} .0753436{col 37}{space 1}    0.36{col 46}{space 3}0.716{col 54}{space 4}-.1202628{col 67}{space 3} .1750785
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1227316{col 26}{space 2} .0615196{col 37}{space 1}    1.99{col 46}{space 3}0.046{col 54}{space 4} .0021554{col 67}{space 3} .2433079
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0530795{col 26}{space 2} .0617494{col 37}{space 1}    0.86{col 46}{space 3}0.390{col 54}{space 4} -.067947{col 67}{space 3}  .174106
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1798958{col 26}{space 2} .0668092{col 37}{space 1}    2.69{col 46}{space 3}0.007{col 54}{space 4} .0489523{col 67}{space 3} .3108394
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .0289102{col 26}{space 2} .0628141{col 37}{space 1}    0.46{col 46}{space 3}0.645{col 54}{space 4}-.0942031{col 67}{space 3} .1520235
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) ciopts(fcolor(gs12) lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> title("Germany", size(large) position(12)) scheme(538bw) ///
> ytitle("") saving(germanyline, replace) yscale(range(-.1 0.4))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file germanyline.gph saved)

{com}. 
.  *Italy
. logit SupportChinaBi i.pr1##i.year female age agesq i.unedu if ccode ==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3630.9669}  
Iteration 1:{space 3}log likelihood = {res:-3588.4704}  
Iteration 2:{space 3}log likelihood = {res:-3588.1407}  
Iteration 3:{space 3}log likelihood = {res: -3588.139}  
Iteration 4:{space 3}log likelihood = {res: -3588.139}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,707
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     85.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -3588.139{txt}{col 49}Pseudo R2{col 67}= {res}    0.0118

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   SupportChinaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .4218008{col 31}{space 2} .3710987{col 42}{space 1}    1.14{col 51}{space 3}0.256{col 59}{space 4}-.3055393{col 72}{space 3} 1.149141
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0103091{col 31}{space 2} .0961927{col 42}{space 1}    0.11{col 51}{space 3}0.915{col 59}{space 4}-.1782252{col 72}{space 3} .1988434
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.1753993{col 31}{space 2} .1041904{col 42}{space 1}   -1.68{col 51}{space 3}0.092{col 59}{space 4}-.3796087{col 72}{space 3} .0288101
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .4865479{col 31}{space 2} .0997309{col 42}{space 1}    4.88{col 51}{space 3}0.000{col 59}{space 4} .2910789{col 72}{space 3}  .682017
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .0383475{col 31}{space 2} .0998489{col 42}{space 1}    0.38{col 51}{space 3}0.701{col 59}{space 4}-.1573527{col 72}{space 3} .2340476
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}  .058054{col 31}{space 2} .1056962{col 42}{space 1}    0.55{col 51}{space 3}0.583{col 59}{space 4}-.1491068{col 72}{space 3} .2652148
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-1.147025{col 31}{space 2} .6698371{col 42}{space 1}   -1.71{col 51}{space 3}0.087{col 59}{space 4}-2.459882{col 72}{space 3} .1658311
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2}-1.359333{col 31}{space 2} .6552802{col 42}{space 1}   -2.07{col 51}{space 3}0.038{col 59}{space 4}-2.643659{col 72}{space 3}-.0750076
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}-.8909509{col 31}{space 2} .4895592{col 42}{space 1}   -1.82{col 51}{space 3}0.069{col 59}{space 4}-1.850469{col 72}{space 3} .0685675
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}-.1117287{col 31}{space 2}  .456219{col 42}{space 1}   -0.24{col 51}{space 3}0.807{col 59}{space 4}-1.005901{col 72}{space 3} .7824441
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} -.497383{col 31}{space 2} .4841381{col 42}{space 1}   -1.03{col 51}{space 3}0.304{col 59}{space 4}-1.446276{col 72}{space 3} .4515102
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.0509922{col 31}{space 2} .0567981{col 42}{space 1}   -0.90{col 51}{space 3}0.369{col 59}{space 4}-.1623145{col 72}{space 3}   .06033
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0065576{col 31}{space 2} .0096181{col 42}{space 1}    0.68{col 51}{space 3}0.495{col 59}{space 4}-.0122936{col 72}{space 3} .0254089
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} -.000122{col 31}{space 2} .0000966{col 42}{space 1}   -1.26{col 51}{space 3}0.207{col 59}{space 4}-.0003114{col 72}{space 3} .0000674
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .1706449{col 31}{space 2} .0647966{col 42}{space 1}    2.63{col 51}{space 3}0.008{col 59}{space 4}  .043646{col 72}{space 3} .2976438
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.8152004{col 31}{space 2} .2370075{col 42}{space 1}   -3.44{col 51}{space 3}0.001{col 59}{space 4}-1.279727{col 72}{space 3}-.3506742
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012/2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     5,707
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportChinaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0969636{col 26}{space 2} .0892749{col 37}{space 1}    1.09{col 46}{space 3}0.277{col 54}{space 4} -.078012{col 67}{space 3} .2719392
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1339337{col 26}{space 2} .0848511{col 37}{space 1}   -1.58{col 46}{space 3}0.114{col 54}{space 4}-.3002388{col 67}{space 3} .0323714
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1479123{col 26}{space 2} .0633518{col 37}{space 1}   -2.33{col 46}{space 3}0.020{col 54}{space 4}-.2720795{col 67}{space 3} -.023745
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1089104{col 26}{space 2} .0697276{col 37}{space 1}   -1.56{col 46}{space 3}0.118{col 54}{space 4} -.245574{col 67}{space 3} .0277532
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0710359{col 26}{space 2} .0628441{col 37}{space 1}    1.13{col 46}{space 3}0.258{col 54}{space 4}-.0521363{col 67}{space 3} .1942081
{txt}{space 10}6  {c |}{col 14}{res}{space 2}-.0164217{col 26}{space 2} .0667534{col 37}{space 1}   -0.25{col 46}{space 3}0.806{col 54}{space 4}-.1472559{col 67}{space 3} .1144124
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(gs7) lpattern(dash) lwidth(medthick))   ///
> recastci(rline) ciopts(fcolor(gs12) lpattern(dash) lwidth(medthick)) plotopt(msym(o) msize(vlarge)) ///
> xlabel(, nogrid) ///
> ylabel(, nogrid) ///
> title("Italy", size(large) position(12))  scheme(538bw) ///
> ytitle("") saving(italyline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file italyline.gph saved)

{com}.  
.  
. gr combine ukline.gph franceline.gph germanyline.gph italyline.gph, ycommon iscale(0.8) /// 
> l1("Marginal Effect", size(small)) title(Favorability Toward China)
{res}{txt}
{com}. graph export "chinadydx_2", as(eps) replace
{txt}(file chinadydx_2 written in EPS format)

{com}. 
. **************
. *Blame Russia*
. **************
. 
. mlogit blame3 pr1##i.c1  female age agesq i.unedu  

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3659.2076}  
Iteration 1:{space 3}log likelihood = {res:-3426.4925}  
Iteration 2:{space 3}log likelihood = {res:-3393.4492}  
Iteration 3:{space 3}log likelihood = {res:-3391.1995}  
Iteration 4:{space 3}log likelihood = {res:-3391.1844}  
Iteration 5:{space 3}log likelihood = {res:-3391.1844}  
{res}
{txt}Multinomial logistic regression{col 49}Number of obs{col 67}= {res}     3,916
{txt}{col 49}LR chi2({res}22{txt}){col 67}= {res}    536.05
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3391.1844{txt}{col 49}Pseudo R2{col 67}= {res}    0.0732

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}             blame3{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Russia             {col 21}{txt}{c |}  (base outcome)
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}West_Ukraine        {txt}{c |}
{space 16}pr1 {c |}
{space 15}AEP  {c |}{col 21}{res}{space 2} 1.917179{col 33}{space 2} .2888841{col 44}{space 1}    6.64{col 53}{space 3}0.000{col 61}{space 4} 1.350976{col 74}{space 3} 2.483381
{txt}{space 19} {c |}
{space 17}c1 {c |}
{space 4}National Front  {c |}{col 21}{res}{space 2}-.0821392{col 33}{space 2} .1266566{col 44}{space 1}   -0.65{col 53}{space 3}0.517{col 61}{space 4}-.3303816{col 74}{space 3} .1661032
{txt}{space 14}UKIP  {c |}{col 21}{res}{space 2}-.2690108{col 33}{space 2} .1416134{col 44}{space 1}   -1.90{col 53}{space 3}0.057{col 61}{space 4} -.546568{col 74}{space 3} .0085463
{txt}{space 9}Lega Nord  {c |}{col 21}{res}{space 2}-.0869226{col 33}{space 2} .1399355{col 44}{space 1}   -0.62{col 53}{space 3}0.534{col 61}{space 4}-.3611912{col 74}{space 3} .1873459
{txt}{space 19} {c |}
{space 13}pr1#c1 {c |}
AEP#National Front  {c |}{col 21}{res}{space 2}-1.152739{col 33}{space 2} .3728163{col 44}{space 1}   -3.09{col 53}{space 3}0.002{col 61}{space 4}-1.883446{col 74}{space 3}-.4220326
{txt}{space 10}AEP#UKIP  {c |}{col 21}{res}{space 2}-1.517124{col 33}{space 2} .4299666{col 44}{space 1}   -3.53{col 53}{space 3}0.000{col 61}{space 4}-2.359843{col 74}{space 3}-.6744046
{txt}{space 5}AEP#Lega Nord  {c |}{col 21}{res}{space 2}-2.492286{col 33}{space 2} .5716512{col 44}{space 1}   -4.36{col 53}{space 3}0.000{col 61}{space 4}-3.612702{col 74}{space 3} -1.37187
{txt}{space 19} {c |}
{space 13}female {c |}{col 21}{res}{space 2} -.189459{col 33}{space 2} .0905582{col 44}{space 1}   -2.09{col 53}{space 3}0.036{col 61}{space 4}-.3669498{col 74}{space 3}-.0119682
{txt}{space 16}age {c |}{col 21}{res}{space 2} -.023447{col 33}{space 2} .0135729{col 44}{space 1}   -1.73{col 53}{space 3}0.084{col 61}{space 4}-.0500495{col 74}{space 3} .0031554
{txt}{space 14}agesq {c |}{col 21}{res}{space 2} .0001621{col 33}{space 2} .0001339{col 44}{space 1}    1.21{col 53}{space 3}0.226{col 61}{space 4}-.0001003{col 74}{space 3} .0004245
{txt}{space 19} {c |}
{space 14}unedu {c |}
{space 2}Higher Education  {c |}{col 21}{res}{space 2}-.2747013{col 33}{space 2}  .095586{col 44}{space 1}   -2.87{col 53}{space 3}0.004{col 61}{space 4}-.4620464{col 74}{space 3}-.0873561
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.3672953{col 33}{space 2} .3373753{col 44}{space 1}   -1.09{col 53}{space 3}0.276{col 61}{space 4}-1.028539{col 74}{space 3} .2939481
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Other               {txt}{c |}
{space 16}pr1 {c |}
{space 15}AEP  {c |}{col 21}{res}{space 2} .2556492{col 33}{space 2}  .381706{col 44}{space 1}    0.67{col 53}{space 3}0.503{col 61}{space 4}-.4924807{col 74}{space 3} 1.003779
{txt}{space 19} {c |}
{space 17}c1 {c |}
{space 4}National Front  {c |}{col 21}{res}{space 2}-2.313457{col 33}{space 2} .2004781{col 44}{space 1}  -11.54{col 53}{space 3}0.000{col 61}{space 4}-2.706387{col 74}{space 3}-1.920527
{txt}{space 14}UKIP  {c |}{col 21}{res}{space 2}-.0538889{col 33}{space 2} .1132881{col 44}{space 1}   -0.48{col 53}{space 3}0.634{col 61}{space 4}-.2759296{col 74}{space 3} .1681518
{txt}{space 9}Lega Nord  {c |}{col 21}{res}{space 2} .4402312{col 33}{space 2} .1092025{col 44}{space 1}    4.03{col 53}{space 3}0.000{col 61}{space 4} .2261983{col 74}{space 3} .6542642
{txt}{space 19} {c |}
{space 13}pr1#c1 {c |}
AEP#National Front  {c |}{col 21}{res}{space 2}-.5380365{col 33}{space 2}   .83588{col 44}{space 1}   -0.64{col 53}{space 3}0.520{col 61}{space 4}-2.176331{col 74}{space 3} 1.100258
{txt}{space 10}AEP#UKIP  {c |}{col 21}{res}{space 2}-.0925612{col 33}{space 2} .4703921{col 44}{space 1}   -0.20{col 53}{space 3}0.844{col 61}{space 4}-1.014513{col 74}{space 3} .8293904
{txt}{space 5}AEP#Lega Nord  {c |}{col 21}{res}{space 2}-.6908697{col 33}{space 2} .5056819{col 44}{space 1}   -1.37{col 53}{space 3}0.172{col 61}{space 4}-1.681988{col 74}{space 3} .3002485
{txt}{space 19} {c |}
{space 13}female {c |}{col 21}{res}{space 2} .3954224{col 33}{space 2} .0834578{col 44}{space 1}    4.74{col 53}{space 3}0.000{col 61}{space 4} .2318482{col 74}{space 3} .5589967
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0034223{col 33}{space 2} .0135832{col 44}{space 1}    0.25{col 53}{space 3}0.801{col 61}{space 4}-.0232003{col 74}{space 3} .0300449
{txt}{space 14}agesq {c |}{col 21}{res}{space 2}-.0000553{col 33}{space 2} .0001334{col 44}{space 1}   -0.41{col 53}{space 3}0.678{col 61}{space 4}-.0003167{col 74}{space 3} .0002061
{txt}{space 19} {c |}
{space 14}unedu {c |}
{space 2}Higher Education  {c |}{col 21}{res}{space 2}-.2666374{col 33}{space 2} .0893009{col 44}{space 1}   -2.99{col 53}{space 3}0.003{col 61}{space 4}-.4416639{col 74}{space 3}-.0916108
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.9273065{col 33}{space 2} .3373197{col 44}{space 1}   -2.75{col 53}{space 3}0.006{col 61}{space 4}-1.588441{col 74}{space 3}-.2661721
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins c1, dydx(pr1) predict(outcome(1)) //Pro-russian separatists
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     3,916
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(blame3==Russia), predict(outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1           {txt}{c |}
{space 13}c1 {c |}
{space 5}Die Linke  {c |}{col 17}{res}{space 2} -.289429{col 29}{space 2} .0561358{col 40}{space 1}   -5.16{col 49}{space 3}0.000{col 57}{space 4}-.3994531{col 70}{space 3}-.1794049
{txt}National Front  {c |}{col 17}{res}{space 2}-.1358836{col 29}{space 2} .0514366{col 40}{space 1}   -2.64{col 49}{space 3}0.008{col 57}{space 4}-.2366975{col 70}{space 3}-.0350697
{txt}{space 10}UKIP  {c |}{col 17}{res}{space 2}-.0601719{col 29}{space 2} .0574152{col 40}{space 1}   -1.05{col 49}{space 3}0.295{col 57}{space 4}-.1727035{col 70}{space 3} .0523597
{txt}{space 5}Lega Nord  {c |}{col 17}{res}{space 2} .1139658{col 29}{space 2}  .069717{col 40}{space 1}    1.63{col 49}{space 3}0.102{col 57}{space 4} -.022677{col 70}{space 3} .2506086
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, horiz xline(0, lcolor(black) lpattern(dash) lwidth(medthick) ) ///
> recast(scatter) recastci(rspike) ciopts(fcolor(gs12)  lwidth(thick)) /// 
> plotopt(msym(o) msize(vlarge)) ytitle("") xtitle("Marginal Effect") /// 
> title("", size(large)) saving(blamerus1, replace) scheme(538bw)

{text}{p 2 6 2}Variables that uniquely identify margins: c1{p_end}
{res}{txt}(file blamerus1.gph saved)

{com}. graph export "blamemargins", as(eps) replace
{txt}(file blamemargins written in EPS format)

{com}. 
. ***************************
. *Arming Ukranian Goverment*
. ***************************
. 
. logit arm_ukraine pr1##i.c1  female age agesq i.unedu 

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -2352.113}  
Iteration 1:{space 3}log likelihood = {res: -2224.587}  
Iteration 2:{space 3}log likelihood = {res:-2223.4277}  
Iteration 3:{space 3}log likelihood = {res:-2223.4257}  
Iteration 4:{space 3}log likelihood = {res:-2223.4257}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,638
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}    257.37
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2223.4257{txt}{col 49}Pseudo R2{col 67}= {res}    0.0547

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        arm_ukraine{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 16}pr1 {c |}
{space 15}AEP  {c |}{col 21}{res}{space 2} -.537137{col 33}{space 2} .3518104{col 44}{space 1}   -1.53{col 53}{space 3}0.127{col 61}{space 4}-1.226673{col 74}{space 3} .1523987
{txt}{space 19} {c |}
{space 17}c1 {c |}
{space 4}National Front  {c |}{col 21}{res}{space 2} 1.033594{col 33}{space 2} .1059006{col 44}{space 1}    9.76{col 53}{space 3}0.000{col 61}{space 4} .8260324{col 74}{space 3} 1.241155
{txt}{space 14}UKIP  {c |}{col 21}{res}{space 2} 1.340659{col 33}{space 2} .1094669{col 44}{space 1}   12.25{col 53}{space 3}0.000{col 61}{space 4} 1.126108{col 74}{space 3} 1.555211
{txt}{space 9}Lega Nord  {c |}{col 21}{res}{space 2} .2040451{col 33}{space 2} .1165554{col 44}{space 1}    1.75{col 53}{space 3}0.080{col 61}{space 4}-.0243994{col 74}{space 3} .4324895
{txt}{space 19} {c |}
{space 13}pr1#c1 {c |}
AEP#National Front  {c |}{col 21}{res}{space 2} -.312799{col 33}{space 2} .4325592{col 44}{space 1}   -0.72{col 53}{space 3}0.470{col 61}{space 4}  -1.1606{col 74}{space 3} .5350015
{txt}{space 10}AEP#UKIP  {c |}{col 21}{res}{space 2} .2152547{col 33}{space 2} .4288154{col 44}{space 1}    0.50{col 53}{space 3}0.616{col 61}{space 4}-.6252081{col 74}{space 3} 1.055717
{txt}{space 5}AEP#Lega Nord  {c |}{col 21}{res}{space 2} 1.042538{col 33}{space 2} .4857165{col 44}{space 1}    2.15{col 53}{space 3}0.032{col 61}{space 4} .0905512{col 74}{space 3} 1.994525
{txt}{space 19} {c |}
{space 13}female {c |}{col 21}{res}{space 2}-.2115651{col 33}{space 2} .0726161{col 44}{space 1}   -2.91{col 53}{space 3}0.004{col 61}{space 4}-.3538901{col 74}{space 3}-.0692402
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0008645{col 33}{space 2} .0112217{col 44}{space 1}    0.08{col 53}{space 3}0.939{col 61}{space 4}-.0211296{col 74}{space 3} .0228586
{txt}{space 14}agesq {c |}{col 21}{res}{space 2}-.0000214{col 33}{space 2} .0001096{col 44}{space 1}   -0.20{col 53}{space 3}0.845{col 61}{space 4}-.0002363{col 74}{space 3} .0001935
{txt}{space 19} {c |}
{space 14}unedu {c |}
{space 2}Higher Education  {c |}{col 21}{res}{space 2} .1203969{col 33}{space 2} .0763667{col 44}{space 1}    1.58{col 53}{space 3}0.115{col 61}{space 4}-.0292791{col 74}{space 3} .2700729
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.204076{col 33}{space 2} .2846672{col 44}{space 1}   -4.23{col 53}{space 3}0.000{col 61}{space 4}-1.762013{col 74}{space 3}-.6461382
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins c1, dydx(pr1)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     3,638
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(arm_ukraine), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1           {txt}{c |}
{space 13}c1 {c |}
{space 5}Die Linke  {c |}{col 17}{res}{space 2}-.0781768{col 29}{space 2} .0436945{col 40}{space 1}   -1.79{col 49}{space 3}0.074{col 57}{space 4}-.1638164{col 70}{space 3} .0074627
{txt}National Front  {c |}{col 17}{res}{space 2}-.1881625{col 29}{space 2} .0484792{col 40}{space 1}   -3.88{col 49}{space 3}0.000{col 57}{space 4}-.2831799{col 70}{space 3} -.093145
{txt}{space 10}UKIP  {c |}{col 17}{res}{space 2}-.0798106{col 29}{space 2} .0602377{col 40}{space 1}   -1.32{col 49}{space 3}0.185{col 57}{space 4}-.1978743{col 70}{space 3} .0382531
{txt}{space 5}Lega Nord  {c |}{col 17}{res}{space 2} .1068752{col 29}{space 2} .0765225{col 40}{space 1}    1.40{col 49}{space 3}0.163{col 57}{space 4} -.043106{col 70}{space 3} .2568565
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, horiz xline(0, lcolor(black) lpattern(dash) lwidth(medthick)) ///
> recast(scatter) recastci(rspike) ciopts(fcolor(gs12)  lwidth(thick)) /// 
> plotopt(msym(o) msize(vlarge)) ytitle("") xtitle("Marginal Effect") /// 
> title("", size(large)) saving(armukraine, replace) scheme(538bw)

{text}{p 2 6 2}Variables that uniquely identify margins: c1{p_end}
{res}{txt}(note: file armukraine.gph not found)
{res}{txt}(file armukraine.gph saved)

{com}. graph export "armukraine", as(eps) replace
{txt}(note: file armukraine not found)
(file armukraine written in EPS format)

{com}. 
. *********************
. *Sanctions on Russia*
. *********************
. 
. mlogit sanctions  pr1##i.c1  female age agesq i.unedu 

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3718.4168}  
Iteration 1:{space 3}log likelihood = {res:-3578.0062}  
Iteration 2:{space 3}log likelihood = {res:-3573.2275}  
Iteration 3:{space 3}log likelihood = {res:-3573.1248}  
Iteration 4:{space 3}log likelihood = {res:-3573.1246}  
{res}
{txt}Multinomial logistic regression{col 49}Number of obs{col 67}= {res}     3,594
{txt}{col 49}LR chi2({res}22{txt}){col 67}= {res}    290.58
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3573.1246{txt}{col 49}Pseudo R2{col 67}= {res}    0.0391

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          sanctions{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Decreased           {txt}{c |}
{space 16}pr1 {c |}
{space 15}AEP  {c |}{col 21}{res}{space 2} 1.903019{col 33}{space 2} .3253473{col 44}{space 1}    5.85{col 53}{space 3}0.000{col 61}{space 4}  1.26535{col 74}{space 3} 2.540688
{txt}{space 19} {c |}
{space 17}c1 {c |}
{space 4}National Front  {c |}{col 21}{res}{space 2}-.1024136{col 33}{space 2} .1161003{col 44}{space 1}   -0.88{col 53}{space 3}0.378{col 61}{space 4} -.329966{col 74}{space 3} .1251389
{txt}{space 14}UKIP  {c |}{col 21}{res}{space 2}-1.025005{col 33}{space 2} .1430896{col 44}{space 1}   -7.16{col 53}{space 3}0.000{col 61}{space 4}-1.305455{col 74}{space 3}-.7445541
{txt}{space 9}Lega Nord  {c |}{col 21}{res}{space 2} .0353146{col 33}{space 2} .1281181{col 44}{space 1}    0.28{col 53}{space 3}0.783{col 61}{space 4}-.2157922{col 74}{space 3} .2864214
{txt}{space 19} {c |}
{space 13}pr1#c1 {c |}
AEP#National Front  {c |}{col 21}{res}{space 2}-1.043133{col 33}{space 2} .4185016{col 44}{space 1}   -2.49{col 53}{space 3}0.013{col 61}{space 4}-1.863381{col 74}{space 3}-.2228851
{txt}{space 10}AEP#UKIP  {c |}{col 21}{res}{space 2}-1.702322{col 33}{space 2} .4960065{col 44}{space 1}   -3.43{col 53}{space 3}0.001{col 61}{space 4}-2.674477{col 74}{space 3}-.7301672
{txt}{space 5}AEP#Lega Nord  {c |}{col 21}{res}{space 2}-1.478477{col 33}{space 2} .5214498{col 44}{space 1}   -2.84{col 53}{space 3}0.005{col 61}{space 4}-2.500499{col 74}{space 3}-.4564538
{txt}{space 19} {c |}
{space 13}female {c |}{col 21}{res}{space 2}-.4916713{col 33}{space 2} .0877923{col 44}{space 1}   -5.60{col 53}{space 3}0.000{col 61}{space 4}-.6637411{col 74}{space 3}-.3196016
{txt}{space 16}age {c |}{col 21}{res}{space 2}-.0103921{col 33}{space 2} .0133414{col 44}{space 1}   -0.78{col 53}{space 3}0.436{col 61}{space 4}-.0365408{col 74}{space 3} .0157566
{txt}{space 14}agesq {c |}{col 21}{res}{space 2} .0001682{col 33}{space 2} .0001282{col 44}{space 1}    1.31{col 53}{space 3}0.190{col 61}{space 4}-.0000831{col 74}{space 3} .0004195
{txt}{space 19} {c |}
{space 14}unedu {c |}
{space 2}Higher Education  {c |}{col 21}{res}{space 2} .0524828{col 33}{space 2} .0917911{col 44}{space 1}    0.57{col 53}{space 3}0.567{col 61}{space 4}-.1274245{col 74}{space 3} .2323901
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}  -.41612{col 33}{space 2} .3396793{col 44}{space 1}   -1.23{col 53}{space 3}0.221{col 61}{space 4}-1.081879{col 74}{space 3} .2496392
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}About_the_same     {col 21}{txt}{c |}  (base outcome)
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}Increased           {txt}{c |}
{space 16}pr1 {c |}
{space 15}AEP  {c |}{col 21}{res}{space 2} .6151835{col 33}{space 2} .4432512{col 44}{space 1}    1.39{col 53}{space 3}0.165{col 61}{space 4} -.253573{col 74}{space 3}  1.48394
{txt}{space 19} {c |}
{space 17}c1 {c |}
{space 4}National Front  {c |}{col 21}{res}{space 2} .2243413{col 33}{space 2}  .121448{col 44}{space 1}    1.85{col 53}{space 3}0.065{col 61}{space 4}-.0136925{col 74}{space 3} .4623751
{txt}{space 14}UKIP  {c |}{col 21}{res}{space 2} .2164445{col 33}{space 2} .1215236{col 44}{space 1}    1.78{col 53}{space 3}0.075{col 61}{space 4}-.0217374{col 74}{space 3} .4546265
{txt}{space 9}Lega Nord  {c |}{col 21}{res}{space 2} .8305143{col 33}{space 2} .1229016{col 44}{space 1}    6.76{col 53}{space 3}0.000{col 61}{space 4} .5896317{col 74}{space 3} 1.071397
{txt}{space 19} {c |}
{space 13}pr1#c1 {c |}
AEP#National Front  {c |}{col 21}{res}{space 2}-.0914088{col 33}{space 2} .5220998{col 44}{space 1}   -0.18{col 53}{space 3}0.861{col 61}{space 4}-1.114706{col 74}{space 3} .9318881
{txt}{space 10}AEP#UKIP  {c |}{col 21}{res}{space 2}-.4728399{col 33}{space 2} .5219149{col 44}{space 1}   -0.91{col 53}{space 3}0.365{col 61}{space 4}-1.495774{col 74}{space 3} .5500945
{txt}{space 5}AEP#Lega Nord  {c |}{col 21}{res}{space 2}-.5788851{col 33}{space 2} .5890406{col 44}{space 1}   -0.98{col 53}{space 3}0.326{col 61}{space 4}-1.733384{col 74}{space 3} .5756133
{txt}{space 19} {c |}
{space 13}female {c |}{col 21}{res}{space 2}-.2188918{col 33}{space 2} .0808923{col 44}{space 1}   -2.71{col 53}{space 3}0.007{col 61}{space 4}-.3774378{col 74}{space 3}-.0603457
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0183872{col 33}{space 2} .0130558{col 44}{space 1}    1.41{col 53}{space 3}0.159{col 61}{space 4}-.0072018{col 74}{space 3} .0439762
{txt}{space 14}agesq {c |}{col 21}{res}{space 2}-.0002674{col 33}{space 2} .0001303{col 44}{space 1}   -2.05{col 53}{space 3}0.040{col 61}{space 4}-.0005228{col 74}{space 3}-.0000121
{txt}{space 19} {c |}
{space 14}unedu {c |}
{space 2}Higher Education  {c |}{col 21}{res}{space 2}-.0677432{col 33}{space 2} .0854925{col 44}{space 1}   -0.79{col 53}{space 3}0.428{col 61}{space 4}-.2353054{col 74}{space 3}  .099819
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.010075{col 33}{space 2} .3227928{col 44}{space 1}   -3.13{col 53}{space 3}0.002{col 61}{space 4}-1.642737{col 74}{space 3}-.3774124
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins c1, dydx(pr1) predict(outcome(0))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     3,594
{txt}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(sanctions==Decreased), predict(outcome(0))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29} Delta-method
{col 17}{c |}      dy/dx{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1           {txt}{c |}
{space 13}c1 {c |}
{space 5}Die Linke  {c |}{col 17}{res}{space 2} .3913742{col 29}{space 2} .0593923{col 40}{space 1}    6.59{col 49}{space 3}0.000{col 57}{space 4} .2749674{col 70}{space 3}  .507781
{txt}National Front  {c |}{col 17}{res}{space 2}  .136357{col 29}{space 2} .0522692{col 40}{space 1}    2.61{col 49}{space 3}0.009{col 57}{space 4} .0339112{col 70}{space 3} .2388028
{txt}{space 10}UKIP  {c |}{col 17}{res}{space 2} .0159951{col 29}{space 2} .0395274{col 40}{space 1}    0.40{col 49}{space 3}0.686{col 57}{space 4}-.0614771{col 70}{space 3} .0934674
{txt}{space 5}Lega Nord  {c |}{col 17}{res}{space 2}    .0767{col 29}{space 2} .0730984{col 40}{space 1}    1.05{col 49}{space 3}0.294{col 57}{space 4}-.0665703{col 70}{space 3} .2199702
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 81}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, horiz xline(0, lcolor(black) lpattern(dash) lwidth(medthick)) ///
> recast(scatter) recastci(rspike) ciopts(fcolor(gs12)  lwidth(thick)) /// 
> plotopt(msym(o) msize(vlarge) ) ytitle("") xtitle("Marginal Effect") /// 
> title("", size(large)) saving(sanctions, replace) scheme(538bw)

{text}{p 2 6 2}Variables that uniquely identify margins: c1{p_end}
{res}{txt}(file sanctions.gph saved)

{com}. graph export "sanctions", as(eps) replace
{txt}(file sanctions written in EPS format)

{com}. 
. ***************
. *Summary Table*
. ***************
. 
. *Generate generation variable
. *Cohorts
. gen gen3 = 1 if age <30 & age >17
{txt}(21,317 missing values generated)

{com}. replace gen3 = 2 if age >29 & age <51
{txt}(8,453 real changes made)

{com}. replace gen3 = 3 if age >50
{txt}(12,864 real changes made)

{com}. label define gen3  1 "18-29" 2 "30-49" 3 "50+", replace
{txt}
{com}. label values gen3 gen3
{txt}
{com}. label variable gen3 "Age Cohort"
{txt}
{com}. 
. tabout PutinBi SupportRussiaBi SupportUSBi SupportNatoBi SupportChinaBi pr1 year female unedu gen3 using summarytable2.tex, /// 
> replace  f(1c) oneway c(freq col) ///
> style(tex) bt font(bold) topf(top.tex) botf(bot.tex)topstr(11cm) 
{res}
{txt}Table output written to: summarytable2.tex

{res}\begin{c -(}center{c )-}
\footnotesize
\newcolumntype{c -(}Y{c )-}{c -(}>{c -(}\raggedleft\arraybackslash{c )-}X{c )-}
\begin{c -(}tabularx{c )-} {c -(}11cm{c )-} {c -(}@{c -(}{c )-} l Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y@{c -(}{c )-}{c )-} \\
\toprule
&No.&\% \\
\midrule
\textbf{c -(}Confidence in Putin{c )-}&& \\
Not Confident&15,646.0&79.2 \\
Confident&4,117.0&20.8 \\
\textbf{c -(}Total{c )-}&19,763.0&100.0 \\
\midrule
\textbf{c -(}Favor Russia{c )-}&& \\
Not Favorable&13,096.0&69.7 \\
Favorable&5,686.0&30.3 \\
\textbf{c -(}Total{c )-}&18,782.0&100.0 \\
\midrule
\textbf{c -(}Favor US{c )-}&& \\
Not Favorable&8,117.0&34.6 \\
Favorable&15,333.0&65.4 \\
\textbf{c -(}Total{c )-}&23,450.0&100.0 \\
\midrule
\textbf{c -(}Favor Nato{c )-}&& \\
Not Favorable&4,418.0&30.4 \\
Favorable&10,111.0&69.6 \\
\textbf{c -(}Total{c )-}&14,529.0&100.0 \\
\midrule
\textbf{c -(}Favor China{c )-}&& \\
Not Favorable&13,543.0&59.9 \\
Favorable&9,069.0&40.1 \\
\textbf{c -(}Total{c )-}&22,612.0&100.0 \\
\midrule
\textbf{c -(}AEP{c )-}&& \\
Other&23,031.0&93.3 \\
AEP&1,662.0&6.7 \\
\textbf{c -(}Total{c )-}&24,693.0&100.0 \\
\midrule
\textbf{c -(}Year{c )-}&& \\
2012&4,096.0&16.6 \\
2013&4,146.0&16.8 \\
2014&4,003.0&16.2 \\
2015&4,000.0&16.2 \\
2016&4,475.0&18.1 \\
2017&3,973.0&16.1 \\
\textbf{c -(}Total{c )-}&24,693.0&100.0 \\
\midrule
\textbf{c -(}Female{c )-}&& \\
0&12,077.0&48.9 \\
1&12,616.0&51.1 \\
\textbf{c -(}Total{c )-}&24,693.0&100.0 \\
\midrule
\textbf{c -(}Education{c )-}&& \\
Lower Education&12,470.0&51.1 \\
Higher Education&11,949.0&48.9 \\
\textbf{c -(}Total{c )-}&24,419.0&100.0 \\
\midrule
\textbf{c -(}Age Cohort{c )-}&& \\
18-29&3,376.0&13.7 \\
30-49&8,453.0&34.2 \\
50+&12,864.0&52.1 \\
\textbf{c -(}Total{c )-}&24,693.0&100.0 \\
\bottomrule
\addlinespace[.75ex]
\end{c -(}tabularx{c )-}
\par
\scriptsize{c -(}\emph{c -(}Source: {c )-}#{c )-}
\normalsize
\end{c -(}center{c )-}
{txt}
{com}. 
. 
. *************************
. *Tables for Main figures*
. *************************
. 
. *********************
. *Confidence in Putin*
. *********************
. 
. *Die Linke
. logit PutinBi i.pr1##i.year female age agesq i.unedu  if ccode==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2757.9254}  
Iteration 1:{space 3}log likelihood = {res:-2644.9441}  
Iteration 2:{space 3}log likelihood = {res:-2642.2455}  
Iteration 3:{space 3}log likelihood = {res:-2642.2434}  
Iteration 4:{space 3}log likelihood = {res:-2642.2434}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,901
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    231.36
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2642.2434{txt}{col 49}Pseudo R2{col 67}= {res}    0.0419

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}  .379474{col 31}{space 2} .3423538{col 42}{space 1}    1.11{col 51}{space 3}0.268{col 59}{space 4}-.2915272{col 72}{space 3} 1.050475
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.1069874{col 31}{space 2} .1166424{col 42}{space 1}   -0.92{col 51}{space 3}0.359{col 59}{space 4}-.3356022{col 72}{space 3} .1216275
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}  -.16243{col 31}{space 2} .1165885{col 42}{space 1}   -1.39{col 51}{space 3}0.164{col 59}{space 4}-.3909392{col 72}{space 3} .0660792
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .4025037{col 31}{space 2} .1090292{col 42}{space 1}    3.69{col 51}{space 3}0.000{col 59}{space 4} .1888104{col 72}{space 3} .6161969
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .2440432{col 31}{space 2} .1102381{col 42}{space 1}    2.21{col 51}{space 3}0.027{col 59}{space 4} .0279805{col 72}{space 3} .4601058
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .7695453{col 31}{space 2} .4264338{col 42}{space 1}    1.80{col 51}{space 3}0.071{col 59}{space 4}-.0662495{col 72}{space 3}  1.60534
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.325229{col 31}{space 2} .4326502{col 42}{space 1}    3.06{col 51}{space 3}0.002{col 59}{space 4} .4772506{col 72}{space 3} 2.173208
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .4325848{col 31}{space 2} .4307544{col 42}{space 1}    1.00{col 51}{space 3}0.315{col 59}{space 4}-.4116783{col 72}{space 3} 1.276848
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .2833297{col 31}{space 2}   .42641{col 42}{space 1}    0.66{col 51}{space 3}0.506{col 59}{space 4}-.5524185{col 72}{space 3} 1.119078
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.5977825{col 31}{space 2} .0690678{col 42}{space 1}   -8.66{col 51}{space 3}0.000{col 59}{space 4}-.7331529{col 72}{space 3} -.462412
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0364712{col 31}{space 2} .0105404{col 42}{space 1}   -3.46{col 51}{space 3}0.001{col 59}{space 4}  -.05713{col 72}{space 3}-.0158124
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0004075{col 31}{space 2} .0001012{col 42}{space 1}    4.03{col 51}{space 3}0.000{col 59}{space 4} .0002093{col 72}{space 3} .0006058
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0066655{col 31}{space 2} .0682685{col 42}{space 1}   -0.10{col 51}{space 3}0.922{col 59}{space 4}-.1404693{col 72}{space 3} .1271382
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.3231358{col 31}{space 2} .2696472{col 42}{space 1}   -1.20{col 51}{space 3}0.231{col 59}{space 4}-.8516346{col 72}{space 3}  .205363
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto die
{txt}
{com}.  
. *National Front
. logit PutinBi i.pr1##i.year female age agesq i.unedu if ccode==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:  -2156.05}  
Iteration 1:{space 3}log likelihood = {res:-2062.8898}  
Iteration 2:{space 3}log likelihood = {res:-2053.5997}  
Iteration 3:{space 3}log likelihood = {res:-2053.5799}  
Iteration 4:{space 3}log likelihood = {res:-2053.5799}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,958
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    204.94
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2053.5799{txt}{col 49}Pseudo R2{col 67}= {res}    0.0475

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .5431333{col 31}{space 2} .3546789{col 42}{space 1}    1.53{col 51}{space 3}0.126{col 59}{space 4}-.1520247{col 72}{space 3} 1.238291
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2} .2342973{col 31}{space 2} .1480777{col 42}{space 1}    1.58{col 51}{space 3}0.114{col 59}{space 4}-.0559296{col 72}{space 3} .5245243
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}  .261887{col 31}{space 2} .1477109{col 42}{space 1}    1.77{col 51}{space 3}0.076{col 59}{space 4}-.0276211{col 72}{space 3}  .551395
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .7612576{col 31}{space 2} .1393271{col 42}{space 1}    5.46{col 51}{space 3}0.000{col 59}{space 4} .4881815{col 72}{space 3} 1.034334
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .5070819{col 31}{space 2}  .142373{col 42}{space 1}    3.56{col 51}{space 3}0.000{col 59}{space 4}  .228036{col 72}{space 3} .7861279
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .2468782{col 31}{space 2} .4397594{col 42}{space 1}    0.56{col 51}{space 3}0.575{col 59}{space 4}-.6150345{col 72}{space 3} 1.108791
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .3657384{col 31}{space 2} .4359346{col 42}{space 1}    0.84{col 51}{space 3}0.401{col 59}{space 4}-.4886778{col 72}{space 3} 1.220155
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .2557511{col 31}{space 2} .4270502{col 42}{space 1}    0.60{col 51}{space 3}0.549{col 59}{space 4} -.581252{col 72}{space 3} 1.092754
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}  .994367{col 31}{space 2} .4370446{col 42}{space 1}    2.28{col 51}{space 3}0.023{col 59}{space 4} .1377753{col 72}{space 3} 1.850959
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.5231611{col 31}{space 2} .0812793{col 42}{space 1}   -6.44{col 51}{space 3}0.000{col 59}{space 4}-.6824657{col 72}{space 3}-.3638565
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0698521{col 31}{space 2} .0112449{col 42}{space 1}   -6.21{col 51}{space 3}0.000{col 59}{space 4}-.0918917{col 72}{space 3}-.0478126
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}  .000701{col 31}{space 2} .0001081{col 42}{space 1}    6.48{col 51}{space 3}0.000{col 59}{space 4} .0004891{col 72}{space 3}  .000913
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1623959{col 31}{space 2} .0837175{col 42}{space 1}   -1.94{col 51}{space 3}0.052{col 59}{space 4}-.3264791{col 72}{space 3} .0016873
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.3462383{col 31}{space 2} .2911723{col 42}{space 1}   -1.19{col 51}{space 3}0.234{col 59}{space 4}-.9169254{col 72}{space 3} .2244489
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto fn
{txt}
{com}.  
. *UKIP
. logit PutinBi i.pr1##i.year female age agesq i.unedu  if ccode==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2434.9586}  
Iteration 1:{space 3}log likelihood = {res:-2374.5405}  
Iteration 2:{space 3}log likelihood = {res:-2372.7909}  
Iteration 3:{space 3}log likelihood = {res:  -2372.79}  
Iteration 4:{space 3}log likelihood = {res:  -2372.79}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,944
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    124.34
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}  -2372.79{txt}{col 49}Pseudo R2{col 67}= {res}    0.0255

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2098011{col 31}{space 2} .4008922{col 42}{space 1}   -0.52{col 51}{space 3}0.601{col 59}{space 4}-.9955354{col 72}{space 3} .5759333
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.0971277{col 31}{space 2} .1227587{col 42}{space 1}   -0.79{col 51}{space 3}0.429{col 59}{space 4}-.3377303{col 72}{space 3} .1434749
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.6806307{col 31}{space 2} .1359025{col 42}{space 1}   -5.01{col 51}{space 3}0.000{col 59}{space 4}-.9469946{col 72}{space 3}-.4142668
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} -.178709{col 31}{space 2}  .113303{col 42}{space 1}   -1.58{col 51}{space 3}0.115{col 59}{space 4}-.4007788{col 72}{space 3} .0433607
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.2631959{col 31}{space 2}  .120347{col 42}{space 1}   -2.19{col 51}{space 3}0.029{col 59}{space 4}-.4990718{col 72}{space 3}-.0273201
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2}  .784164{col 31}{space 2} .4713797{col 42}{space 1}    1.66{col 51}{space 3}0.096{col 59}{space 4}-.1397233{col 72}{space 3} 1.708051
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .5737192{col 31}{space 2} .5059506{col 42}{space 1}    1.13{col 51}{space 3}0.257{col 59}{space 4}-.4179258{col 72}{space 3} 1.565364
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .7916332{col 31}{space 2} .4440965{col 42}{space 1}    1.78{col 51}{space 3}0.075{col 59}{space 4}-.0787799{col 72}{space 3} 1.662046
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} 1.399278{col 31}{space 2} .5627975{col 42}{space 1}    2.49{col 51}{space 3}0.013{col 59}{space 4} .2962147{col 72}{space 3}  2.50234
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.4251279{col 31}{space 2} .0739813{col 42}{space 1}   -5.75{col 51}{space 3}0.000{col 59}{space 4}-.5701285{col 72}{space 3}-.2801273
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0397933{col 31}{space 2} .0105572{col 42}{space 1}   -3.77{col 51}{space 3}0.000{col 59}{space 4} -.060485{col 72}{space 3}-.0191017
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0002935{col 31}{space 2} .0000993{col 42}{space 1}    2.96{col 51}{space 3}0.003{col 59}{space 4}  .000099{col 72}{space 3}  .000488
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.2077077{col 31}{space 2} .0779683{col 42}{space 1}   -2.66{col 51}{space 3}0.008{col 59}{space 4}-.3605228{col 72}{space 3}-.0548926
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .2512954{col 31}{space 2} .2790165{col 42}{space 1}    0.90{col 51}{space 3}0.368{col 59}{space 4}-.2955668{col 72}{space 3} .7981577
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto ukip
{txt}
{com}.  
. *Lega
. logit PutinBi i.pr1##i.year female age agesq i.unedu  if ccode==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2505.1522}  
Iteration 1:{space 3}log likelihood = {res: -2434.478}  
Iteration 2:{space 3}log likelihood = {res: -2432.915}  
Iteration 3:{space 3}log likelihood = {res:-2432.9147}  
Iteration 4:{space 3}log likelihood = {res:-2432.9147}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,620
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    144.48
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2432.9147{txt}{col 49}Pseudo R2{col 67}= {res}    0.0288

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.4567092{col 31}{space 2} .5416035{col 42}{space 1}   -0.84{col 51}{space 3}0.399{col 59}{space 4}-1.518233{col 72}{space 3} .6048141
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.0551994{col 31}{space 2} .1252326{col 42}{space 1}   -0.44{col 51}{space 3}0.659{col 59}{space 4}-.3006507{col 72}{space 3}  .190252
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.0194584{col 31}{space 2} .1252693{col 42}{space 1}   -0.16{col 51}{space 3}0.877{col 59}{space 4}-.2649817{col 72}{space 3} .2260649
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}  .752149{col 31}{space 2} .1120999{col 42}{space 1}    6.71{col 51}{space 3}0.000{col 59}{space 4} .5324372{col 72}{space 3} .9718607
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .6254497{col 31}{space 2} .1188763{col 42}{space 1}    5.26{col 51}{space 3}0.000{col 59}{space 4} .3924564{col 72}{space 3} .8584431
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.807738{col 31}{space 2} .6570834{col 42}{space 1}    2.75{col 51}{space 3}0.006{col 59}{space 4} .5198783{col 72}{space 3} 3.095598
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .9663712{col 31}{space 2} .6385751{col 42}{space 1}    1.51{col 51}{space 3}0.130{col 59}{space 4} -.285213{col 72}{space 3} 2.217955
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} 1.123479{col 31}{space 2} .6006285{col 42}{space 1}    1.87{col 51}{space 3}0.061{col 59}{space 4}-.0537315{col 72}{space 3} 2.300689
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .5402807{col 31}{space 2} .6212583{col 42}{space 1}    0.87{col 51}{space 3}0.384{col 59}{space 4}-.6773632{col 72}{space 3} 1.757925
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.2581362{col 31}{space 2} .0711122{col 42}{space 1}   -3.63{col 51}{space 3}0.000{col 59}{space 4}-.3975135{col 72}{space 3}-.1187589
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0045472{col 31}{space 2} .0121013{col 42}{space 1}    0.38{col 51}{space 3}0.707{col 59}{space 4} -.019171{col 72}{space 3} .0282653
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}-.0000687{col 31}{space 2} .0001198{col 42}{space 1}   -0.57{col 51}{space 3}0.566{col 59}{space 4}-.0003035{col 72}{space 3}  .000166
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0443326{col 31}{space 2} .0812518{col 42}{space 1}    0.55{col 51}{space 3}0.585{col 59}{space 4} -.114918{col 72}{space 3} .2035833
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-1.425025{col 31}{space 2} .3024292{col 42}{space 1}   -4.71{col 51}{space 3}0.000{col 59}{space 4}-2.017775{col 72}{space 3}-.8322742
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto lega
{txt}
{com}.  
. *CONTROLS SHOWN WIDE 
. esttab  die fn ukip lega  ///
> using multilevel.tex, b(%9.2f) se(%9.2f) label ///
> title(Confidence in Putin - Logistic Regression\label{c -(}tab1{c )-}) /// 
> addnotes(Note: Put your notes here.) nonum  ///
> mtitles("Germany" "France" "Great Britain" "Italy") compress star(* 0.05)  replace ///
> stats(N ll chi2 , labels("Observations" "Log Likelihood" "Chi-Squared") fmt(%9.0g)) ///
> nobaselevels interaction(" X ") nogap collabels(,lhs(Fav. Russia)) wide  booktabs 
{res}{txt}(note: file multilevel.tex not found)
(output written to {browse  `"multilevel.tex"'})

{com}. 
. ****************************
. *Favorability Toward Russia*
. ****************************
. 
. *Die Linke
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu  if ccode==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2846.8328}  
Iteration 1:{space 3}log likelihood = {res:-2729.2372}  
Iteration 2:{space 3}log likelihood = {res:-2727.4391}  
Iteration 3:{space 3}log likelihood = {res:-2727.4379}  
Iteration 4:{space 3}log likelihood = {res:-2727.4379}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,802
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    238.79
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2727.4379{txt}{col 49}Pseudo R2{col 67}= {res}    0.0419

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .4792311{col 31}{space 2} .3252968{col 42}{space 1}    1.47{col 51}{space 3}0.141{col 59}{space 4}-.1583388{col 72}{space 3} 1.116801
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.0683836{col 31}{space 2} .1028438{col 42}{space 1}   -0.66{col 51}{space 3}0.506{col 59}{space 4}-.2699538{col 72}{space 3} .1331867
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.7205554{col 31}{space 2} .1126692{col 42}{space 1}   -6.40{col 51}{space 3}0.000{col 59}{space 4} -.941383{col 72}{space 3}-.4997278
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.4486365{col 31}{space 2} .1073144{col 42}{space 1}   -4.18{col 51}{space 3}0.000{col 59}{space 4}-.6589688{col 72}{space 3}-.2383042
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.2088302{col 31}{space 2} .1045615{col 42}{space 1}   -2.00{col 51}{space 3}0.046{col 59}{space 4}-.4137669{col 72}{space 3}-.0038935
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.0845691{col 31}{space 2} .4707738{col 42}{space 1}   -0.18{col 51}{space 3}0.857{col 59}{space 4}-1.007269{col 72}{space 3} .8381305
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .4505122{col 31}{space 2} .4192124{col 42}{space 1}    1.07{col 51}{space 3}0.283{col 59}{space 4}-.3711291{col 72}{space 3} 1.272153
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .9794297{col 31}{space 2} .4177922{col 42}{space 1}    2.34{col 51}{space 3}0.019{col 59}{space 4}  .160572{col 72}{space 3} 1.798287
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}-.1923841{col 31}{space 2} .4195745{col 42}{space 1}   -0.46{col 51}{space 3}0.647{col 59}{space 4}-1.014735{col 72}{space 3} .6299668
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.6705885{col 31}{space 2} .0673927{col 42}{space 1}   -9.95{col 51}{space 3}0.000{col 59}{space 4}-.8026757{col 72}{space 3}-.5385013
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0521879{col 31}{space 2} .0101505{col 42}{space 1}   -5.14{col 51}{space 3}0.000{col 59}{space 4}-.0720825{col 72}{space 3}-.0322933
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005213{col 31}{space 2}  .000099{col 42}{space 1}    5.27{col 51}{space 3}0.000{col 59}{space 4} .0003273{col 72}{space 3} .0007153
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0057898{col 31}{space 2} .0677422{col 42}{space 1}    0.09{col 51}{space 3}0.932{col 59}{space 4}-.1269826{col 72}{space 3} .1385621
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .7207566{col 31}{space 2} .2540361{col 42}{space 1}    2.84{col 51}{space 3}0.005{col 59}{space 4} .2228551{col 72}{space 3} 1.218658
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto die
{txt}
{com}.  
. *National Front
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu  if ccode==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3082.5389}  
Iteration 1:{space 3}log likelihood = {res:-2957.1622}  
Iteration 2:{space 3}log likelihood = {res:-2955.9862}  
Iteration 3:{space 3}log likelihood = {res:-2955.9858}  
Iteration 4:{space 3}log likelihood = {res:-2955.9858}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,978
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    253.11
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2955.9858{txt}{col 49}Pseudo R2{col 67}= {res}    0.0411

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .5365231{col 31}{space 2}   .26923{col 42}{space 1}    1.99{col 51}{space 3}0.046{col 59}{space 4} .0088419{col 72}{space 3} 1.064204
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0127886{col 31}{space 2} .1015006{col 42}{space 1}    0.13{col 51}{space 3}0.900{col 59}{space 4} -.186149{col 72}{space 3} .2117262
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.5698942{col 31}{space 2} .1084513{col 42}{space 1}   -5.25{col 51}{space 3}0.000{col 59}{space 4}-.7824547{col 72}{space 3}-.3573336
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.3087298{col 31}{space 2} .1049462{col 42}{space 1}   -2.94{col 51}{space 3}0.003{col 59}{space 4}-.5144205{col 72}{space 3} -.103039
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .0824445{col 31}{space 2} .1014932{col 42}{space 1}    0.81{col 51}{space 3}0.417{col 59}{space 4}-.1164785{col 72}{space 3} .2813675
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.1111923{col 31}{space 2} .3516018{col 42}{space 1}   -0.32{col 51}{space 3}0.752{col 59}{space 4}-.8003193{col 72}{space 3} .5779346
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .0120262{col 31}{space 2} .3579198{col 42}{space 1}    0.03{col 51}{space 3}0.973{col 59}{space 4}-.6894836{col 72}{space 3} .7135361
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .3102993{col 31}{space 2} .3499358{col 42}{space 1}    0.89{col 51}{space 3}0.375{col 59}{space 4}-.3755623{col 72}{space 3} .9961609
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .6214469{col 31}{space 2} .3703837{col 42}{space 1}    1.68{col 51}{space 3}0.093{col 59}{space 4}-.1044918{col 72}{space 3} 1.347386
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.3916533{col 31}{space 2} .0631293{col 42}{space 1}   -6.20{col 51}{space 3}0.000{col 59}{space 4}-.5153845{col 72}{space 3}-.2679221
{txt}{space 14}age {c |}{col 19}{res}{space 2} -.069363{col 31}{space 2}  .009065{col 42}{space 1}   -7.65{col 51}{space 3}0.000{col 59}{space 4}  -.08713{col 72}{space 3} -.051596
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005824{col 31}{space 2} .0000885{col 42}{space 1}    6.58{col 51}{space 3}0.000{col 59}{space 4} .0004088{col 72}{space 3} .0007559
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} -.343497{col 31}{space 2} .0661803{col 42}{space 1}   -5.19{col 51}{space 3}0.000{col 59}{space 4}-.4732081{col 72}{space 3}-.2137859
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.437398{col 31}{space 2} .2294726{col 42}{space 1}    6.26{col 51}{space 3}0.000{col 59}{space 4} .9876396{col 72}{space 3} 1.887156
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto fn
{txt}
{com}.  
. *UKIP
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu  if ccode==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2590.6948}  
Iteration 1:{space 3}log likelihood = {res:-2442.3558}  
Iteration 2:{space 3}log likelihood = {res:-2440.8358}  
Iteration 3:{space 3}log likelihood = {res:-2440.8346}  
Iteration 4:{space 3}log likelihood = {res:-2440.8346}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,080
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    299.72
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2440.8346{txt}{col 49}Pseudo R2{col 67}= {res}    0.0578

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.3772472{col 31}{space 2} .3305464{col 42}{space 1}   -1.14{col 51}{space 3}0.254{col 59}{space 4}-1.025106{col 72}{space 3} .2706119
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0406138{col 31}{space 2} .1080521{col 42}{space 1}    0.38{col 51}{space 3}0.707{col 59}{space 4}-.1711645{col 72}{space 3} .2523921
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.8922894{col 31}{space 2} .1123983{col 42}{space 1}   -7.94{col 51}{space 3}0.000{col 59}{space 4}-1.112586{col 72}{space 3}-.6719927
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-1.293838{col 31}{space 2} .1204386{col 42}{space 1}  -10.74{col 51}{space 3}0.000{col 59}{space 4}-1.529894{col 72}{space 3}-1.057783
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.8213998{col 31}{space 2} .1081767{col 42}{space 1}   -7.59{col 51}{space 3}0.000{col 59}{space 4}-1.033422{col 72}{space 3}-.6093775
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .5718393{col 31}{space 2} .4068896{col 42}{space 1}    1.41{col 51}{space 3}0.160{col 59}{space 4}-.2256496{col 72}{space 3} 1.369328
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .6480467{col 31}{space 2} .4201326{col 42}{space 1}    1.54{col 51}{space 3}0.123{col 59}{space 4}-.1753982{col 72}{space 3} 1.471491
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .7750749{col 31}{space 2} .4386767{col 42}{space 1}    1.77{col 51}{space 3}0.077{col 59}{space 4}-.0847157{col 72}{space 3} 1.634865
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .8227637{col 31}{space 2} .5451768{col 42}{space 1}    1.51{col 51}{space 3}0.131{col 59}{space 4}-.2457633{col 72}{space 3} 1.891291
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.3046417{col 31}{space 2} .0695255{col 42}{space 1}   -4.38{col 51}{space 3}0.000{col 59}{space 4}-.4409092{col 72}{space 3}-.1683742
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0586194{col 31}{space 2} .0103881{col 42}{space 1}   -5.64{col 51}{space 3}0.000{col 59}{space 4}-.0789797{col 72}{space 3}-.0382591
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0004537{col 31}{space 2} .0000992{col 42}{space 1}    4.57{col 51}{space 3}0.000{col 59}{space 4} .0002592{col 72}{space 3} .0006481
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1021148{col 31}{space 2} .0757489{col 42}{space 1}   -1.35{col 51}{space 3}0.178{col 59}{space 4}  -.25058{col 72}{space 3} .0463504
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.736742{col 31}{space 2}  .270469{col 42}{space 1}    6.42{col 51}{space 3}0.000{col 59}{space 4} 1.206633{col 72}{space 3} 2.266852
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto ukip
{txt}
{com}.  
. *Lega
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu  if ccode==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2828.0253}  
Iteration 1:{space 3}log likelihood = {res:-2765.9931}  
Iteration 2:{space 3}log likelihood = {res:-2765.4747}  
Iteration 3:{space 3}log likelihood = {res:-2765.4742}  
Iteration 4:{space 3}log likelihood = {res:-2765.4742}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,681
{txt}{col 49}LR chi2({res}13{txt}){col 67}= {res}    125.10
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2765.4742{txt}{col 49}Pseudo R2{col 67}= {res}    0.0221

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} -.784342{col 31}{space 2} .5430626{col 42}{space 1}   -1.44{col 51}{space 3}0.149{col 59}{space 4}-1.848725{col 72}{space 3} .2800412
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .4953104{col 31}{space 2} .1004309{col 42}{space 1}    4.93{col 51}{space 3}0.000{col 59}{space 4} .2984695{col 72}{space 3} .6921513
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.2452439{col 31}{space 2} .1153378{col 42}{space 1}   -2.13{col 51}{space 3}0.033{col 59}{space 4}-.4713019{col 72}{space 3}-.0191859
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}  .085253{col 31}{space 2} .1108525{col 42}{space 1}    0.77{col 51}{space 3}0.442{col 59}{space 4}-.1320139{col 72}{space 3} .3025199
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .6381002{col 31}{space 2} .1088526{col 42}{space 1}    5.86{col 51}{space 3}0.000{col 59}{space 4} .4247531{col 72}{space 3} .8514473
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} 1.120981{col 31}{space 2} .7035113{col 42}{space 1}    1.59{col 51}{space 3}0.111{col 59}{space 4}-.2578757{col 72}{space 3} 2.499838
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.523363{col 31}{space 2} .6640351{col 42}{space 1}    2.29{col 51}{space 3}0.022{col 59}{space 4} .2218783{col 72}{space 3} 2.824848
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.291839{col 31}{space 2} .6245787{col 42}{space 1}    2.07{col 51}{space 3}0.039{col 59}{space 4}  .067687{col 72}{space 3}  2.51599
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .7725306{col 31}{space 2} .6211774{col 42}{space 1}    1.24{col 51}{space 3}0.214{col 59}{space 4}-.4449547{col 72}{space 3} 1.990016
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.1220961{col 31}{space 2} .0653589{col 42}{space 1}   -1.87{col 51}{space 3}0.062{col 59}{space 4}-.2501972{col 72}{space 3} .0060051
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0124937{col 31}{space 2} .0109694{col 42}{space 1}   -1.14{col 51}{space 3}0.255{col 59}{space 4}-.0339934{col 72}{space 3} .0090059
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0000564{col 31}{space 2} .0001104{col 42}{space 1}    0.51{col 51}{space 3}0.609{col 59}{space 4}  -.00016{col 72}{space 3} .0002728
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .1164204{col 31}{space 2}  .074294{col 42}{space 1}    1.57{col 51}{space 3}0.117{col 59}{space 4}-.0291932{col 72}{space 3}  .262034
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.6263182{col 31}{space 2} .2684915{col 42}{space 1}   -2.33{col 51}{space 3}0.020{col 59}{space 4}-1.152552{col 72}{space 3}-.1000844
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto lega
{txt}
{com}.  
. *CONTROLS SHOWN WIDE 
. esttab  die fn ukip lega  ///
> using multilevel.tex, b(%9.2f) se(%9.2f) label ///
> title(Favorability towards Russia - Logistic Regression\label{c -(}tab1{c )-}) /// 
> addnotes(Note: Put your notes here.) nonum  ///
> mtitles("Germany" "France" "Great Britain" "Italy") compress star(* 0.05)  replace ///
> stats(N ll chi2 , labels("Observations" "Log Likelihood" "Chi-Squared") fmt(%9.0g)) ///
> nobaselevels interaction(" X ") nogap collabels(,lhs(Fav. Russia)) wide  booktabs 
{res}{txt}(output written to {browse  `"multilevel.tex"'})

{com}. 
. *************************
. *Favorability Toward U.S*
. *************************
. 
. *Die Linke
. logit SupportUSBi i.pr1##i.year female age agesq i.unedu  if ccode==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3990.5744}  
Iteration 1:{space 3}log likelihood = {res:-3881.5409}  
Iteration 2:{space 3}log likelihood = {res:-3881.0723}  
Iteration 3:{space 3}log likelihood = {res:-3881.0707}  
Iteration 4:{space 3}log likelihood = {res:-3881.0707}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,760
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    219.01
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3881.0707{txt}{col 49}Pseudo R2{col 67}= {res}    0.0274

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      SupportUSBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-1.064485{col 31}{space 2} .3372419{col 42}{space 1}   -3.16{col 51}{space 3}0.002{col 59}{space 4}-1.725467{col 72}{space 3}-.4035031
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0528761{col 31}{space 2} .0945321{col 42}{space 1}    0.56{col 51}{space 3}0.576{col 59}{space 4}-.1324034{col 72}{space 3} .2381555
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.0891718{col 31}{space 2} .0942971{col 42}{space 1}   -0.95{col 51}{space 3}0.344{col 59}{space 4}-.2739907{col 72}{space 3} .0956471
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .0015542{col 31}{space 2} .0948475{col 42}{space 1}    0.02{col 51}{space 3}0.987{col 59}{space 4}-.1843435{col 72}{space 3}  .187452
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .2197042{col 31}{space 2} .0964418{col 42}{space 1}    2.28{col 51}{space 3}0.023{col 59}{space 4} .0306818{col 72}{space 3} .4087265
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.7347127{col 31}{space 2}  .096028{col 42}{space 1}   -7.65{col 51}{space 3}0.000{col 59}{space 4}-.9229242{col 72}{space 3}-.5465013
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .1136245{col 31}{space 2} .4833379{col 42}{space 1}    0.24{col 51}{space 3}0.814{col 59}{space 4}-.8337005{col 72}{space 3} 1.060949
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2}-.1518395{col 31}{space 2} .4386612{col 42}{space 1}   -0.35{col 51}{space 3}0.729{col 59}{space 4}  -1.0116{col 72}{space 3} .7079207
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}-.6992308{col 31}{space 2} .4686828{col 42}{space 1}   -1.49{col 51}{space 3}0.136{col 59}{space 4}-1.617832{col 72}{space 3} .2193706
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .1640304{col 31}{space 2} .4320968{col 42}{space 1}    0.38{col 51}{space 3}0.704{col 59}{space 4}-.6828638{col 72}{space 3} 1.010925
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .5703487{col 31}{space 2} .4365344{col 42}{space 1}    1.31{col 51}{space 3}0.191{col 59}{space 4} -.285243{col 72}{space 3}  1.42594
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.0194675{col 31}{space 2} .0541256{col 42}{space 1}   -0.36{col 51}{space 3}0.719{col 59}{space 4}-.1255518{col 72}{space 3} .0866168
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0233561{col 31}{space 2} .0086999{col 42}{space 1}   -2.68{col 51}{space 3}0.007{col 59}{space 4}-.0404075{col 72}{space 3}-.0063047
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0002259{col 31}{space 2} .0000846{col 42}{space 1}    2.67{col 51}{space 3}0.008{col 59}{space 4} .0000601{col 72}{space 3} .0003917
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0060478{col 31}{space 2} .0550193{col 42}{space 1}    0.11{col 51}{space 3}0.912{col 59}{space 4}-.1017879{col 72}{space 3} .1138836
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .7492903{col 31}{space 2} .2209302{col 42}{space 1}    3.39{col 51}{space 3}0.001{col 59}{space 4} .3162751{col 72}{space 3} 1.182306
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto die
{txt}
{com}.  
. *National Front
. logit SupportUSBi i.pr1##i.year female age agesq i.unedu  if ccode==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3813.1253}  
Iteration 1:{space 3}log likelihood = {res:-3670.8238}  
Iteration 2:{space 3}log likelihood = {res:-3670.0574}  
Iteration 3:{space 3}log likelihood = {res:-3670.0573}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,906
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    286.14
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3670.0573{txt}{col 49}Pseudo R2{col 67}= {res}    0.0375

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      SupportUSBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .1330205{col 31}{space 2} .2944145{col 42}{space 1}    0.45{col 51}{space 3}0.651{col 59}{space 4}-.4440214{col 72}{space 3} .7100624
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.2081221{col 31}{space 2} .0996802{col 42}{space 1}   -2.09{col 51}{space 3}0.037{col 59}{space 4}-.4034916{col 72}{space 3}-.0127526
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} .2097351{col 31}{space 2} .1037285{col 42}{space 1}    2.02{col 51}{space 3}0.043{col 59}{space 4}  .006431{col 72}{space 3} .4130391
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .1315911{col 31}{space 2} .1030842{col 42}{space 1}    1.28{col 51}{space 3}0.202{col 59}{space 4}-.0704502{col 72}{space 3} .3336325
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}-.0379321{col 31}{space 2} .1025197{col 42}{space 1}   -0.37{col 51}{space 3}0.711{col 59}{space 4} -.238867{col 72}{space 3} .1630027
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-1.115926{col 31}{space 2} .0982852{col 42}{space 1}  -11.35{col 51}{space 3}0.000{col 59}{space 4}-1.308562{col 72}{space 3}-.9232908
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.2665079{col 31}{space 2} .3728709{col 42}{space 1}   -0.71{col 51}{space 3}0.475{col 59}{space 4}-.9973214{col 72}{space 3} .4643057
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} -.232099{col 31}{space 2} .3842902{col 42}{space 1}   -0.60{col 51}{space 3}0.546{col 59}{space 4}-.9852939{col 72}{space 3} .5210959
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}-.3943312{col 31}{space 2} .3748346{col 42}{space 1}   -1.05{col 51}{space 3}0.293{col 59}{space 4}-1.128994{col 72}{space 3} .3403312
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .1173752{col 31}{space 2} .3890027{col 42}{space 1}    0.30{col 51}{space 3}0.763{col 59}{space 4}-.6450561{col 72}{space 3} .8798065
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} 1.016147{col 31}{space 2}  .396515{col 42}{space 1}    2.56{col 51}{space 3}0.010{col 59}{space 4} .2389921{col 72}{space 3} 1.793302
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}  .039608{col 31}{space 2} .0562876{col 42}{space 1}    0.70{col 51}{space 3}0.482{col 59}{space 4}-.0707136{col 72}{space 3} .1499295
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0372525{col 31}{space 2} .0086223{col 42}{space 1}   -4.32{col 51}{space 3}0.000{col 59}{space 4}-.0541519{col 72}{space 3}-.0203531
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0003333{col 31}{space 2} .0000837{col 42}{space 1}    3.98{col 51}{space 3}0.000{col 59}{space 4} .0001693{col 72}{space 3} .0004973
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0178882{col 31}{space 2} .0583536{col 42}{space 1}   -0.31{col 51}{space 3}0.759{col 59}{space 4}-.1322592{col 72}{space 3} .0964829
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.718511{col 31}{space 2} .2220393{col 42}{space 1}    7.74{col 51}{space 3}0.000{col 59}{space 4} 1.283322{col 72}{space 3}   2.1537
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto fn
{txt}
{com}.  
. *UKIP
. logit SupportUSBi i.pr1##i.year female age agesq i.unedu  if ccode==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3562.6679}  
Iteration 1:{space 3}log likelihood = {res:-3503.4572}  
Iteration 2:{space 3}log likelihood = {res:-3503.2105}  
Iteration 3:{space 3}log likelihood = {res:-3503.2104}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,614
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    118.91
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3503.2104{txt}{col 49}Pseudo R2{col 67}= {res}    0.0167

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      SupportUSBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .0319972{col 31}{space 2} .3269557{col 42}{space 1}    0.10{col 51}{space 3}0.922{col 59}{space 4}-.6088242{col 72}{space 3} .6728187
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0329389{col 31}{space 2} .1058448{col 42}{space 1}    0.31{col 51}{space 3}0.756{col 59}{space 4} -.174513{col 72}{space 3} .2403909
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} .2519108{col 31}{space 2}  .106458{col 42}{space 1}    2.37{col 51}{space 3}0.018{col 59}{space 4} .0432569{col 72}{space 3} .4605647
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .3427494{col 31}{space 2} .1097457{col 42}{space 1}    3.12{col 51}{space 3}0.002{col 59}{space 4} .1276516{col 72}{space 3} .5578471
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .2899091{col 31}{space 2} .0997841{col 42}{space 1}    2.91{col 51}{space 3}0.004{col 59}{space 4} .0943359{col 72}{space 3} .4854823
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.4788857{col 31}{space 2} .0996486{col 42}{space 1}   -4.81{col 51}{space 3}0.000{col 59}{space 4}-.6741934{col 72}{space 3} -.283578
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .0295981{col 31}{space 2} .4072882{col 42}{space 1}    0.07{col 51}{space 3}0.942{col 59}{space 4}-.7686722{col 72}{space 3} .8278683
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .1973582{col 31}{space 2} .4199258{col 42}{space 1}    0.47{col 51}{space 3}0.638{col 59}{space 4}-.6256812{col 72}{space 3} 1.020398
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .1357126{col 31}{space 2} .4415784{col 42}{space 1}    0.31{col 51}{space 3}0.759{col 59}{space 4}-.7297652{col 72}{space 3}  1.00119
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}-.4962018{col 31}{space 2} .3778848{col 42}{space 1}   -1.31{col 51}{space 3}0.189{col 59}{space 4}-1.236842{col 72}{space 3} .2444388
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .3171339{col 31}{space 2} .5344384{col 42}{space 1}    0.59{col 51}{space 3}0.553{col 59}{space 4}-.7303461{col 72}{space 3} 1.364614
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.1825647{col 31}{space 2} .0577522{col 42}{space 1}   -3.16{col 51}{space 3}0.002{col 59}{space 4} -.295757{col 72}{space 3}-.0693724
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0197748{col 31}{space 2} .0087925{col 42}{space 1}   -2.25{col 51}{space 3}0.025{col 59}{space 4}-.0370077{col 72}{space 3}-.0025419
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0001861{col 31}{space 2} .0000824{col 42}{space 1}    2.26{col 51}{space 3}0.024{col 59}{space 4} .0000246{col 72}{space 3} .0003476
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1475301{col 31}{space 2} .0620345{col 42}{space 1}   -2.38{col 51}{space 3}0.017{col 59}{space 4}-.2691155{col 72}{space 3}-.0259448
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.287188{col 31}{space 2} .2356785{col 42}{space 1}    5.46{col 51}{space 3}0.000{col 59}{space 4} .8252671{col 72}{space 3}  1.74911
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto ukip
{txt}
{com}.  
. *Lega
. logit SupportUSBi i.pr1##i.year female age agesq i.unedu  if ccode==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3061.8686}  
Iteration 1:{space 3}log likelihood = {res:-2968.5147}  
Iteration 2:{space 3}log likelihood = {res:-2965.4169}  
Iteration 3:{space 3}log likelihood = {res:-2965.3891}  
Iteration 4:{space 3}log likelihood = {res: -2965.389}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,803
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    192.96
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -2965.389{txt}{col 49}Pseudo R2{col 67}= {res}    0.0315

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      SupportUSBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.5570918{col 31}{space 2} .3816355{col 42}{space 1}   -1.46{col 51}{space 3}0.144{col 59}{space 4}-1.305084{col 72}{space 3}    .1909
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .3998245{col 31}{space 2} .1136772{col 42}{space 1}    3.52{col 51}{space 3}0.000{col 59}{space 4} .1770213{col 72}{space 3} .6226277
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} .2286941{col 31}{space 2} .1166989{col 42}{space 1}    1.96{col 51}{space 3}0.050{col 59}{space 4}-.0000315{col 72}{space 3} .4574196
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .5226055{col 31}{space 2} .1223994{col 42}{space 1}    4.27{col 51}{space 3}0.000{col 59}{space 4} .2827072{col 72}{space 3} .7625039
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} -.039562{col 31}{space 2} .1096206{col 42}{space 1}   -0.36{col 51}{space 3}0.718{col 59}{space 4}-.2544145{col 72}{space 3} .1752905
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.6358716{col 31}{space 2} .1089111{col 42}{space 1}   -5.84{col 51}{space 3}0.000{col 59}{space 4}-.8493335{col 72}{space 3}-.4224096
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}  .402875{col 31}{space 2} .6835492{col 42}{space 1}    0.59{col 51}{space 3}0.556{col 59}{space 4}-.9368568{col 72}{space 3} 1.742607
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.914878{col 31}{space 2} .8288663{col 42}{space 1}    2.31{col 51}{space 3}0.021{col 59}{space 4} .2903297{col 72}{space 3} 3.539426
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.647589{col 31}{space 2} .7139053{col 42}{space 1}    2.31{col 51}{space 3}0.021{col 59}{space 4} .2483604{col 72}{space 3} 3.046818
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .2454069{col 31}{space 2} .4740019{col 42}{space 1}    0.52{col 51}{space 3}0.605{col 59}{space 4}-.6836198{col 72}{space 3} 1.174434
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .6811217{col 31}{space 2} .4857988{col 42}{space 1}    1.40{col 51}{space 3}0.161{col 59}{space 4}-.2710265{col 72}{space 3}  1.63327
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2} .2768176{col 31}{space 2} .0646177{col 42}{space 1}    4.28{col 51}{space 3}0.000{col 59}{space 4} .1501692{col 72}{space 3} .4034659
{txt}{space 14}age {c |}{col 19}{res}{space 2} -.020957{col 31}{space 2} .0109775{col 42}{space 1}   -1.91{col 51}{space 3}0.056{col 59}{space 4}-.0424726{col 72}{space 3} .0005585
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0001464{col 31}{space 2} .0001079{col 42}{space 1}    1.36{col 51}{space 3}0.175{col 59}{space 4}-.0000651{col 72}{space 3} .0003579
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}  .058177{col 31}{space 2} .0735955{col 42}{space 1}    0.79{col 51}{space 3}0.429{col 59}{space 4}-.0860676{col 72}{space 3} .2024216
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.670804{col 31}{space 2} .2767579{col 42}{space 1}    6.04{col 51}{space 3}0.000{col 59}{space 4} 1.128369{col 72}{space 3}  2.21324
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto lega
{txt}
{com}.  
. *CONTROLS SHOWN WIDE 
. esttab  die fn ukip lega  ///
> using multilevel.tex, b(%9.2f) se(%9.2f) label ///
> title(Favorability towards Russia - Logistic Regression\label{c -(}tab1{c )-}) /// 
> addnotes(Note: Put your notes here.) nonum  ///
> mtitles("Germany" "France" "Great Britain" "Italy") compress star(* 0.05)  replace ///
> stats(N ll chi2 , labels("Observations" "Log Likelihood" "Chi-Squared") fmt(%9.0g)) ///
> nobaselevels interaction(" X ") nogap collabels(,lhs(Fav. Russia)) wide  booktabs 
{res}{txt}(output written to {browse  `"multilevel.tex"'})

{com}. 
. **************************
. *Favorability Toward NATO*
. *************************
. 
. *Die Linke
. logit SupportNatoBi i.pr1##i.year female age agesq i.unedu  if ccode==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2350.3673}  
Iteration 1:{space 3}log likelihood = {res:-2292.8847}  
Iteration 2:{space 3}log likelihood = {res:-2292.5639}  
Iteration 3:{space 3}log likelihood = {res:-2292.5637}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,728
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}    115.61
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-2292.5637{txt}{col 49}Pseudo R2{col 67}= {res}    0.0246

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    SupportNatoBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} -1.35891{col 31}{space 2} .3220339{col 42}{space 1}   -4.22{col 51}{space 3}0.000{col 59}{space 4}-1.990085{col 72}{space 3}-.7277353
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0046552{col 31}{space 2} .1034414{col 42}{space 1}    0.05{col 51}{space 3}0.964{col 59}{space 4}-.1980862{col 72}{space 3} .2073966
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.1597494{col 31}{space 2} .1020976{col 42}{space 1}   -1.56{col 51}{space 3}0.118{col 59}{space 4}-.3598571{col 72}{space 3} .0403583
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .2396611{col 31}{space 2} .1063876{col 42}{space 1}    2.25{col 51}{space 3}0.024{col 59}{space 4} .0311453{col 72}{space 3}  .448177
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}  .151413{col 31}{space 2} .4705195{col 42}{space 1}    0.32{col 51}{space 3}0.748{col 59}{space 4}-.7707883{col 72}{space 3} 1.073614
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}-.3388613{col 31}{space 2}  .434455{col 42}{space 1}   -0.78{col 51}{space 3}0.435{col 59}{space 4}-1.190377{col 72}{space 3} .5126548
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .1828757{col 31}{space 2} .4111431{col 42}{space 1}    0.44{col 51}{space 3}0.656{col 59}{space 4}  -.62295{col 72}{space 3} .9887014
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.1498807{col 31}{space 2} .0716405{col 42}{space 1}   -2.09{col 51}{space 3}0.036{col 59}{space 4}-.2902935{col 72}{space 3}-.0094679
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0019663{col 31}{space 2} .0115916{col 42}{space 1}   -0.17{col 51}{space 3}0.865{col 59}{space 4}-.0246855{col 72}{space 3} .0207528
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}-.0000118{col 31}{space 2} .0001126{col 42}{space 1}   -0.11{col 51}{space 3}0.916{col 59}{space 4}-.0002326{col 72}{space 3} .0002089
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0743581{col 31}{space 2} .0734749{col 42}{space 1}    1.01{col 51}{space 3}0.312{col 59}{space 4}-.0696501{col 72}{space 3} .2183662
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  .970944{col 31}{space 2} .2894604{col 42}{space 1}    3.35{col 51}{space 3}0.001{col 59}{space 4}  .403612{col 72}{space 3} 1.538276
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto die
{txt}
{com}.  
. *National Front
. logit SupportNatoBi i.pr1##i.year female age agesq i.unedu  if ccode==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2524.2724}  
Iteration 1:{space 3}log likelihood = {res:-2506.8281}  
Iteration 2:{space 3}log likelihood = {res: -2506.789}  
Iteration 3:{space 3}log likelihood = {res: -2506.789}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,904
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}     34.97
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log likelihood = {res} -2506.789{txt}{col 49}Pseudo R2{col 67}= {res}    0.0069

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    SupportNatoBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2236292{col 31}{space 2} .2774377{col 42}{space 1}   -0.81{col 51}{space 3}0.420{col 59}{space 4} -.767397{col 72}{space 3} .3201386
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.2613833{col 31}{space 2} .0998285{col 42}{space 1}   -2.62{col 51}{space 3}0.009{col 59}{space 4}-.4570435{col 72}{space 3} -.065723
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} -.128415{col 31}{space 2} .1009584{col 42}{space 1}   -1.27{col 51}{space 3}0.203{col 59}{space 4}-.3262898{col 72}{space 3} .0694598
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.1750508{col 31}{space 2} .1011319{col 42}{space 1}   -1.73{col 51}{space 3}0.083{col 59}{space 4}-.3732657{col 72}{space 3} .0231641
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.3344945{col 31}{space 2} .3557189{col 42}{space 1}   -0.94{col 51}{space 3}0.347{col 59}{space 4}-1.031691{col 72}{space 3} .3627018
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}  .159858{col 31}{space 2} .3596159{col 42}{space 1}    0.44{col 51}{space 3}0.657{col 59}{space 4}-.5449761{col 72}{space 3} .8646921
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}  .116462{col 31}{space 2} .3808129{col 42}{space 1}    0.31{col 51}{space 3}0.760{col 59}{space 4}-.6299176{col 72}{space 3} .8628415
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}  .007892{col 31}{space 2} .0676877{col 42}{space 1}    0.12{col 51}{space 3}0.907{col 59}{space 4}-.1247735{col 72}{space 3} .1405574
{txt}{space 14}age {c |}{col 19}{res}{space 2} -.030014{col 31}{space 2} .0102726{col 42}{space 1}   -2.92{col 51}{space 3}0.003{col 59}{space 4} -.050148{col 72}{space 3}-.0098801
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0002718{col 31}{space 2} .0000999{col 42}{space 1}    2.72{col 51}{space 3}0.007{col 59}{space 4}  .000076{col 72}{space 3} .0004676
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}  .148335{col 31}{space 2} .0704747{col 42}{space 1}    2.10{col 51}{space 3}0.035{col 59}{space 4} .0102072{col 72}{space 3} .2864629
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.445422{col 31}{space 2} .2601813{col 42}{space 1}    5.56{col 51}{space 3}0.000{col 59}{space 4} .9354757{col 72}{space 3} 1.955368
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto fn
{txt}
{com}.  
. *UKIP
. logit SupportNatoBi i.pr1##i.year female age agesq i.unedu  if ccode==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1675.0423}  
Iteration 1:{space 3}log likelihood = {res:-1662.0257}  
Iteration 2:{space 3}log likelihood = {res:  -1661.71}  
Iteration 3:{space 3}log likelihood = {res:  -1661.71}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,146
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}     26.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0052
{txt}Log likelihood = {res}  -1661.71{txt}{col 49}Pseudo R2{col 67}= {res}    0.0080

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    SupportNatoBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.4355938{col 31}{space 2} .3465319{col 42}{space 1}   -1.26{col 51}{space 3}0.209{col 59}{space 4}-1.114784{col 72}{space 3} .2435962
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0020388{col 31}{space 2} .1284388{col 42}{space 1}    0.02{col 51}{space 3}0.987{col 59}{space 4}-.2496967{col 72}{space 3} .2537743
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .0215209{col 31}{space 2} .1277561{col 42}{space 1}    0.17{col 51}{space 3}0.866{col 59}{space 4}-.2288766{col 72}{space 3} .2719183
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .1899894{col 31}{space 2} .1253635{col 42}{space 1}    1.52{col 51}{space 3}0.130{col 59}{space 4}-.0557186{col 72}{space 3} .4356974
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}  .213816{col 31}{space 2} .4362995{col 42}{space 1}    0.49{col 51}{space 3}0.624{col 59}{space 4}-.6413153{col 72}{space 3} 1.068947
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .8212767{col 31}{space 2} .4892789{col 42}{space 1}    1.68{col 51}{space 3}0.093{col 59}{space 4}-.1376923{col 72}{space 3} 1.780246
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}-.7306262{col 31}{space 2}  .521294{col 42}{space 1}   -1.40{col 51}{space 3}0.161{col 59}{space 4}-1.752344{col 72}{space 3} .2910913
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2} -.113464{col 31}{space 2} .0863524{col 42}{space 1}   -1.31{col 51}{space 3}0.189{col 59}{space 4}-.2827117{col 72}{space 3} .0557836
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0187904{col 31}{space 2} .0132353{col 42}{space 1}    1.42{col 51}{space 3}0.156{col 59}{space 4}-.0071504{col 72}{space 3} .0447312
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}-.0001491{col 31}{space 2} .0001256{col 42}{space 1}   -1.19{col 51}{space 3}0.235{col 59}{space 4}-.0003953{col 72}{space 3} .0000972
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .2894761{col 31}{space 2} .0917587{col 42}{space 1}    3.15{col 51}{space 3}0.002{col 59}{space 4} .1096324{col 72}{space 3} .4693198
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .5670174{col 31}{space 2} .3440199{col 42}{space 1}    1.65{col 51}{space 3}0.099{col 59}{space 4}-.1072493{col 72}{space 3} 1.241284
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto ukip
{txt}
{com}.  
. *Lega
. logit SupportNatoBi i.pr1##i.year female age agesq i.unedu  if ccode==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-2185.2171}  
Iteration 1:{space 3}log likelihood = {res:-2169.0079}  
Iteration 2:{space 3}log likelihood = {res:-2168.9518}  
Iteration 3:{space 3}log likelihood = {res:-2168.9518}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,559
{txt}{col 49}LR chi2({res}11{txt}){col 67}= {res}     32.53
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0006
{txt}Log likelihood = {res}-2168.9518{txt}{col 49}Pseudo R2{col 67}= {res}    0.0074

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    SupportNatoBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2100509{col 31}{space 2} .3803566{col 42}{space 1}   -0.55{col 51}{space 3}0.581{col 59}{space 4}-.9555362{col 72}{space 3} .5354344
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0436309{col 31}{space 2}  .102621{col 42}{space 1}    0.43{col 51}{space 3}0.671{col 59}{space 4}-.1575025{col 72}{space 3} .2447643
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .0741053{col 31}{space 2} .1100944{col 42}{space 1}    0.67{col 51}{space 3}0.501{col 59}{space 4}-.1416757{col 72}{space 3} .2898863
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.2433666{col 31}{space 2} .1110995{col 42}{space 1}   -2.19{col 51}{space 3}0.028{col 59}{space 4}-.4611177{col 72}{space 3}-.0256156
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.1749148{col 31}{space 2} .5943811{col 42}{space 1}   -0.29{col 51}{space 3}0.769{col 59}{space 4} -1.33988{col 72}{space 3} .9900508
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .3740178{col 31}{space 2} .5153779{col 42}{space 1}    0.73{col 51}{space 3}0.468{col 59}{space 4}-.6361043{col 72}{space 3}  1.38414
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} 1.087346{col 31}{space 2}  .537555{col 42}{space 1}    2.02{col 51}{space 3}0.043{col 59}{space 4} .0337573{col 72}{space 3} 2.140934
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2} .2576795{col 31}{space 2} .0734272{col 42}{space 1}    3.51{col 51}{space 3}0.000{col 59}{space 4} .1137648{col 72}{space 3} .4015942
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0197587{col 31}{space 2}   .01257{col 42}{space 1}   -1.57{col 51}{space 3}0.116{col 59}{space 4}-.0443955{col 72}{space 3}  .004878
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}  .000234{col 31}{space 2} .0001268{col 42}{space 1}    1.85{col 51}{space 3}0.065{col 59}{space 4}-.0000144{col 72}{space 3} .0004825
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0971003{col 31}{space 2} .0828483{col 42}{space 1}    1.17{col 51}{space 3}0.241{col 59}{space 4}-.0652794{col 72}{space 3}   .25948
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.014034{col 31}{space 2} .3047851{col 42}{space 1}    3.33{col 51}{space 3}0.001{col 59}{space 4} .4166659{col 72}{space 3} 1.611402
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto lega
{txt}
{com}.  
. *CONTROLS SHOWN WIDE 
. esttab  die fn ukip lega  ///
> using multilevel.tex, b(%9.2f) se(%9.2f) label ///
> title(Favorability towards Russia - Logistic Regression\label{c -(}tab1{c )-}) /// 
> addnotes(Note: Put your notes here.) nonum  ///
> mtitles("Germany" "France" "Great Britain" "Italy") compress star(* 0.05)  replace ///
> stats(N ll chi2 , labels("Observations" "Log Likelihood" "Chi-Squared") fmt(%9.0g)) ///
> nobaselevels interaction(" X ") nogap collabels(,lhs(Fav. Russia)) wide  booktabs 
{res}{txt}(output written to {browse  `"multilevel.tex"'})

{com}. 
. ***************************
. *Favorability Toward China*
. ***************************
. 
. *Die Linke
. logit SupportChinaBi i.pr1##i.year female age agesq i.unedu  if ccode==255

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3494.5266}  
Iteration 1:{space 3}log likelihood = {res:-3429.8864}  
Iteration 2:{space 3}log likelihood = {res:-3429.6605}  
Iteration 3:{space 3}log likelihood = {res:-3429.6605}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,529
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    129.73
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3429.6605{txt}{col 49}Pseudo R2{col 67}= {res}    0.0186

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   SupportChinaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .0480711{col 31}{space 2}  .335806{col 42}{space 1}    0.14{col 51}{space 3}0.886{col 59}{space 4}-.6100966{col 72}{space 3} .7062388
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.1748323{col 31}{space 2} .1041141{col 42}{space 1}   -1.68{col 51}{space 3}0.093{col 59}{space 4}-.3788922{col 72}{space 3} .0292275
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.0522718{col 31}{space 2} .1038233{col 42}{space 1}   -0.50{col 51}{space 3}0.615{col 59}{space 4}-.2557618{col 72}{space 3} .1512182
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .1643232{col 31}{space 2} .1018074{col 42}{space 1}    1.61{col 51}{space 3}0.107{col 59}{space 4}-.0352156{col 72}{space 3}  .363862
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}-.0888674{col 31}{space 2} .1053005{col 42}{space 1}   -0.84{col 51}{space 3}0.399{col 59}{space 4}-.2952525{col 72}{space 3} .1175177
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .2265539{col 31}{space 2} .1028329{col 42}{space 1}    2.20{col 51}{space 3}0.028{col 59}{space 4} .0250052{col 72}{space 3} .4281027
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .0854286{col 31}{space 2} .4911121{col 42}{space 1}    0.17{col 51}{space 3}0.862{col 59}{space 4}-.8771335{col 72}{space 3} 1.047991
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .4916643{col 31}{space 2} .4232144{col 42}{space 1}    1.16{col 51}{space 3}0.245{col 59}{space 4}-.3378208{col 72}{space 3} 1.321149
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .1810492{col 31}{space 2} .4253635{col 42}{space 1}    0.43{col 51}{space 3}0.670{col 59}{space 4}-.6526481{col 72}{space 3} 1.014746
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}  .731799{col 31}{space 2} .4343748{col 42}{space 1}    1.68{col 51}{space 3}0.092{col 59}{space 4}-.1195599{col 72}{space 3} 1.583158
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .0763649{col 31}{space 2} .4292713{col 42}{space 1}    0.18{col 51}{space 3}0.859{col 59}{space 4}-.7649915{col 72}{space 3} .9177212
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.3752801{col 31}{space 2}  .058751{col 42}{space 1}   -6.39{col 51}{space 3}0.000{col 59}{space 4}  -.49043{col 72}{space 3}-.2601302
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0475662{col 31}{space 2}  .009088{col 42}{space 1}   -5.23{col 51}{space 3}0.000{col 59}{space 4}-.0653784{col 72}{space 3} -.029754
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005203{col 31}{space 2} .0000881{col 42}{space 1}    5.90{col 51}{space 3}0.000{col 59}{space 4} .0003475{col 72}{space 3} .0006931
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0551523{col 31}{space 2} .0594129{col 42}{space 1}   -0.93{col 51}{space 3}0.353{col 59}{space 4}-.1715995{col 72}{space 3}  .061295
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .3563966{col 31}{space 2} .2310074{col 42}{space 1}    1.54{col 51}{space 3}0.123{col 59}{space 4}-.0963696{col 72}{space 3} .8091627
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto die
{txt}
{com}.  
. *National Front
. logit SupportChinaBi i.pr1##i.year female age agesq i.unedu  if ccode==220

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-4008.3368}  
Iteration 1:{space 3}log likelihood = {res:-3921.7917}  
Iteration 2:{space 3}log likelihood = {res:-3921.7317}  
Iteration 3:{space 3}log likelihood = {res:-3921.7317}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,881
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    173.21
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3921.7317{txt}{col 49}Pseudo R2{col 67}= {res}    0.0216

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   SupportChinaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2577578{col 31}{space 2} .2773738{col 42}{space 1}   -0.93{col 51}{space 3}0.353{col 59}{space 4}-.8014004{col 72}{space 3} .2858848
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .1242957{col 31}{space 2} .0961662{col 42}{space 1}    1.29{col 51}{space 3}0.196{col 59}{space 4}-.0641866{col 72}{space 3} .3127781
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} .2148541{col 31}{space 2} .0957653{col 42}{space 1}    2.24{col 51}{space 3}0.025{col 59}{space 4} .0271575{col 72}{space 3} .4025507
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .4690758{col 31}{space 2} .0957555{col 42}{space 1}    4.90{col 51}{space 3}0.000{col 59}{space 4} .2813985{col 72}{space 3} .6567531
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}-.1136277{col 31}{space 2} .0999583{col 42}{space 1}   -1.14{col 51}{space 3}0.256{col 59}{space 4}-.3095425{col 72}{space 3}  .082287
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .1358484{col 31}{space 2} .0969407{col 42}{space 1}    1.40{col 51}{space 3}0.161{col 59}{space 4}-.0541519{col 72}{space 3} .3258487
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .1520171{col 31}{space 2}  .358018{col 42}{space 1}    0.42{col 51}{space 3}0.671{col 59}{space 4}-.5496854{col 72}{space 3} .8537195
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .3648146{col 31}{space 2} .3557791{col 42}{space 1}    1.03{col 51}{space 3}0.305{col 59}{space 4}-.3324995{col 72}{space 3} 1.062129
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .1849591{col 31}{space 2} .3536405{col 42}{space 1}    0.52{col 51}{space 3}0.601{col 59}{space 4}-.5081636{col 72}{space 3} .8780818
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}  .225082{col 31}{space 2}  .362697{col 42}{space 1}    0.62{col 51}{space 3}0.535{col 59}{space 4} -.485791{col 72}{space 3}  .935955
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}  .898482{col 31}{space 2} .3730053{col 42}{space 1}    2.41{col 51}{space 3}0.016{col 59}{space 4} .1674051{col 72}{space 3} 1.629559
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.1665026{col 31}{space 2} .0537518{col 42}{space 1}   -3.10{col 51}{space 3}0.002{col 59}{space 4}-.2718542{col 72}{space 3}-.0611509
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0654718{col 31}{space 2} .0079468{col 42}{space 1}   -8.24{col 51}{space 3}0.000{col 59}{space 4}-.0810473{col 72}{space 3}-.0498963
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005526{col 31}{space 2} .0000772{col 42}{space 1}    7.16{col 51}{space 3}0.000{col 59}{space 4} .0004014{col 72}{space 3} .0007039
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1620612{col 31}{space 2} .0558789{col 42}{space 1}   -2.90{col 51}{space 3}0.004{col 59}{space 4}-.2715819{col 72}{space 3}-.0525405
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.413815{col 31}{space 2} .2042637{col 42}{space 1}    6.92{col 51}{space 3}0.000{col 59}{space 4} 1.013466{col 72}{space 3} 1.814165
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto fn
{txt}
{com}.  
. *UKIP
. logit SupportChinaBi i.pr1##i.year female age agesq i.unedu  if ccode==200

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3580.7556}  
Iteration 1:{space 3}log likelihood = {res:-3502.4879}  
Iteration 2:{space 3}log likelihood = {res:-3502.3682}  
Iteration 3:{space 3}log likelihood = {res:-3502.3682}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,173
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}    156.77
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-3502.3682{txt}{col 49}Pseudo R2{col 67}= {res}    0.0219

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   SupportChinaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .0602201{col 31}{space 2} .3173956{col 42}{space 1}    0.19{col 51}{space 3}0.850{col 59}{space 4}-.5618638{col 72}{space 3}  .682304
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .1552572{col 31}{space 2} .1079746{col 42}{space 1}    1.44{col 51}{space 3}0.150{col 59}{space 4}-.0563692{col 72}{space 3} .3668836
{txt}{space 12}2014  {c |}{col 19}{res}{space 2} -.151749{col 31}{space 2} .1048819{col 42}{space 1}   -1.45{col 51}{space 3}0.148{col 59}{space 4}-.3573137{col 72}{space 3} .0538157
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.1547923{col 31}{space 2} .1060864{col 42}{space 1}   -1.46{col 51}{space 3}0.145{col 59}{space 4}-.3627177{col 72}{space 3} .0531332
{txt}{space 12}2016  {c |}{col 19}{res}{space 2}-.4407567{col 31}{space 2} .0977208{col 42}{space 1}   -4.51{col 51}{space 3}0.000{col 59}{space 4}-.6322859{col 72}{space 3}-.2492275
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.1807077{col 31}{space 2} .1023595{col 42}{space 1}   -1.77{col 51}{space 3}0.077{col 59}{space 4}-.3813287{col 72}{space 3} .0199133
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.5817005{col 31}{space 2}  .394489{col 42}{space 1}   -1.47{col 51}{space 3}0.140{col 59}{space 4}-1.354885{col 72}{space 3} .1914838
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2}-.1217596{col 31}{space 2} .3953227{col 42}{space 1}   -0.31{col 51}{space 3}0.758{col 59}{space 4}-.8965779{col 72}{space 3} .6530587
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .0628562{col 31}{space 2}  .415749{col 42}{space 1}    0.15{col 51}{space 3}0.880{col 59}{space 4}-.7519968{col 72}{space 3} .8777092
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}-.4203575{col 31}{space 2} .3695754{col 42}{space 1}   -1.14{col 51}{space 3}0.255{col 59}{space 4}-1.144712{col 72}{space 3}  .303997
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}-.3238955{col 31}{space 2} .5265147{col 42}{space 1}   -0.62{col 51}{space 3}0.538{col 59}{space 4}-1.355845{col 72}{space 3} .7080543
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.2932622{col 31}{space 2} .0568192{col 42}{space 1}   -5.16{col 51}{space 3}0.000{col 59}{space 4}-.4046257{col 72}{space 3}-.1818986
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0574745{col 31}{space 2} .0087092{col 42}{space 1}   -6.60{col 51}{space 3}0.000{col 59}{space 4}-.0745443{col 72}{space 3}-.0404047
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0004501{col 31}{space 2} .0000813{col 42}{space 1}    5.54{col 51}{space 3}0.000{col 59}{space 4} .0002908{col 72}{space 3} .0006094
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0904179{col 31}{space 2} .0610785{col 42}{space 1}   -1.48{col 51}{space 3}0.139{col 59}{space 4}-.2101296{col 72}{space 3} .0292938
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.095306{col 31}{space 2} .2359598{col 42}{space 1}    8.88{col 51}{space 3}0.000{col 59}{space 4} 1.632833{col 72}{space 3} 2.557779
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto ukip
{txt}
{com}.  
. *Lega
. logit SupportChinaBi i.pr1##i.year female age agesq i.unedu  if ccode==325

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-3630.9669}  
Iteration 1:{space 3}log likelihood = {res:-3588.4704}  
Iteration 2:{space 3}log likelihood = {res:-3588.1407}  
Iteration 3:{space 3}log likelihood = {res: -3588.139}  
Iteration 4:{space 3}log likelihood = {res: -3588.139}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     5,707
{txt}{col 49}LR chi2({res}15{txt}){col 67}= {res}     85.66
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -3588.139{txt}{col 49}Pseudo R2{col 67}= {res}    0.0118

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   SupportChinaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .4218008{col 31}{space 2} .3710987{col 42}{space 1}    1.14{col 51}{space 3}0.256{col 59}{space 4}-.3055393{col 72}{space 3} 1.149141
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0103091{col 31}{space 2} .0961927{col 42}{space 1}    0.11{col 51}{space 3}0.915{col 59}{space 4}-.1782252{col 72}{space 3} .1988434
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.1753993{col 31}{space 2} .1041904{col 42}{space 1}   -1.68{col 51}{space 3}0.092{col 59}{space 4}-.3796087{col 72}{space 3} .0288101
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .4865479{col 31}{space 2} .0997309{col 42}{space 1}    4.88{col 51}{space 3}0.000{col 59}{space 4} .2910789{col 72}{space 3}  .682017
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .0383475{col 31}{space 2} .0998489{col 42}{space 1}    0.38{col 51}{space 3}0.701{col 59}{space 4}-.1573527{col 72}{space 3} .2340476
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}  .058054{col 31}{space 2} .1056962{col 42}{space 1}    0.55{col 51}{space 3}0.583{col 59}{space 4}-.1491068{col 72}{space 3} .2652148
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-1.147025{col 31}{space 2} .6698371{col 42}{space 1}   -1.71{col 51}{space 3}0.087{col 59}{space 4}-2.459882{col 72}{space 3} .1658311
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2}-1.359333{col 31}{space 2} .6552802{col 42}{space 1}   -2.07{col 51}{space 3}0.038{col 59}{space 4}-2.643659{col 72}{space 3}-.0750076
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2}-.8909509{col 31}{space 2} .4895592{col 42}{space 1}   -1.82{col 51}{space 3}0.069{col 59}{space 4}-1.850469{col 72}{space 3} .0685675
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}-.1117287{col 31}{space 2}  .456219{col 42}{space 1}   -0.24{col 51}{space 3}0.807{col 59}{space 4}-1.005901{col 72}{space 3} .7824441
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} -.497383{col 31}{space 2} .4841381{col 42}{space 1}   -1.03{col 51}{space 3}0.304{col 59}{space 4}-1.446276{col 72}{space 3} .4515102
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.0509922{col 31}{space 2} .0567981{col 42}{space 1}   -0.90{col 51}{space 3}0.369{col 59}{space 4}-.1623145{col 72}{space 3}   .06033
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0065576{col 31}{space 2} .0096181{col 42}{space 1}    0.68{col 51}{space 3}0.495{col 59}{space 4}-.0122936{col 72}{space 3} .0254089
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} -.000122{col 31}{space 2} .0000966{col 42}{space 1}   -1.26{col 51}{space 3}0.207{col 59}{space 4}-.0003114{col 72}{space 3} .0000674
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .1706449{col 31}{space 2} .0647966{col 42}{space 1}    2.63{col 51}{space 3}0.008{col 59}{space 4}  .043646{col 72}{space 3} .2976438
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.8152004{col 31}{space 2} .2370075{col 42}{space 1}   -3.44{col 51}{space 3}0.001{col 59}{space 4}-1.279727{col 72}{space 3}-.3506742
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.  est sto lega
{txt}
{com}.  
. *CONTROLS SHOWN WIDE 
. esttab  die fn ukip lega  ///
> using multilevel.tex, b(%9.2f) se(%9.2f) label ///
> title(Favorability towards Russia - Logistic Regression\label{c -(}tab1{c )-}) /// 
> addnotes(Note: Put your notes here.) nonum  ///
> mtitles("Germany" "France" "Great Britain" "Italy") compress star(* 0.05)  replace ///
> stats(N ll chi2 , labels("Observations" "Log Likelihood" "Chi-Squared") fmt(%9.0g)) ///
> nobaselevels interaction(" X ") nogap collabels(,lhs(Fav. Russia)) wide  booktabs 
{res}{txt}(output written to {browse  `"multilevel.tex"'})

{com}. 
. 
. 
. 
. ****************************
. *Appendix Figures - Weights*
. ****************************
. 
. **********************************
. *Confidence in Putin with Weights*
. **********************************
. 
. *United Kingdom
. logit  PutinBi i.pr1##i.year female age agesq i.unedu [pweight=weight] if ccode ==200

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2523.7983}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2458.7123}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2457.3229}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2457.3223}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2457.3223}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,944
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}     88.57
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2457.3223{txt}{col 49}Pseudo R2{col 67}= {res}    0.0263

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .0544019{col 31}{space 2} .4107263{col 42}{space 1}    0.13{col 51}{space 3}0.895{col 59}{space 4}-.7506068{col 72}{space 3} .8594105
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.1203727{col 31}{space 2} .1332087{col 42}{space 1}   -0.90{col 51}{space 3}0.366{col 59}{space 4} -.381457{col 72}{space 3} .1407115
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.6073207{col 31}{space 2} .1588485{col 42}{space 1}   -3.82{col 51}{space 3}0.000{col 59}{space 4} -.918658{col 72}{space 3}-.2959833
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} -.176557{col 31}{space 2}   .13556{col 42}{space 1}   -1.30{col 51}{space 3}0.193{col 59}{space 4}-.4422497{col 72}{space 3} .0891358
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.2595188{col 31}{space 2} .1382041{col 42}{space 1}   -1.88{col 51}{space 3}0.060{col 59}{space 4}-.5303938{col 72}{space 3} .0113562
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .5496948{col 31}{space 2} .4845067{col 42}{space 1}    1.13{col 51}{space 3}0.257{col 59}{space 4}-.3999208{col 72}{space 3}  1.49931
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .0679837{col 31}{space 2} .5617084{col 42}{space 1}    0.12{col 51}{space 3}0.904{col 59}{space 4}-1.032944{col 72}{space 3} 1.168912
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .6011759{col 31}{space 2} .4720469{col 42}{space 1}    1.27{col 51}{space 3}0.203{col 59}{space 4}-.3240191{col 72}{space 3} 1.526371
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .7225303{col 31}{space 2} .6009101{col 42}{space 1}    1.20{col 51}{space 3}0.229{col 59}{space 4}-.4552319{col 72}{space 3} 1.900292
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.4814804{col 31}{space 2} .0873739{col 42}{space 1}   -5.51{col 51}{space 3}0.000{col 59}{space 4}-.6527302{col 72}{space 3}-.3102307
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0313921{col 31}{space 2} .0119088{col 42}{space 1}   -2.64{col 51}{space 3}0.008{col 59}{space 4}-.0547328{col 72}{space 3}-.0080513
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}  .000216{col 31}{space 2} .0001129{col 42}{space 1}    1.91{col 51}{space 3}0.056{col 59}{space 4}-5.21e-06{col 72}{space 3} .0004372
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1613189{col 31}{space 2} .0937748{col 42}{space 1}   -1.72{col 51}{space 3}0.085{col 59}{space 4}-.3451142{col 72}{space 3} .0224764
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} .0515669{col 31}{space 2} .3104295{col 42}{space 1}    0.17{col 51}{space 3}0.868{col 59}{space 4}-.5568638{col 72}{space 3} .6599976
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,944
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0097311{col 26}{space 2} .0743693{col 37}{space 1}    0.13{col 46}{space 3}0.896{col 54}{space 4}-.1360301{col 67}{space 3} .1554923
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1159515{col 26}{space 2} .0543517{col 37}{space 1}    2.13{col 46}{space 3}0.033{col 54}{space 4} .0094242{col 67}{space 3} .2224789
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0155729{col 26}{space 2} .0505564{col 37}{space 1}    0.31{col 46}{space 3}0.758{col 54}{space 4}-.0835158{col 67}{space 3} .1146616
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1240882{col 26}{space 2} .0488344{col 37}{space 1}    2.54{col 46}{space 3}0.011{col 54}{space 4} .0283746{col 67}{space 3} .2198018
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1454189{col 26}{space 2}  .094929{col 37}{space 1}    1.53{col 46}{space 3}0.126{col 54}{space 4}-.0406385{col 67}{space 3} .3314762
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(black) lpattern(dash) lwidth(medthick) ) recastci(rline) /// 
> ciopts(fcolor(gs12)  lwidth(thick)) plotopt(msym(o) msize(vlarge)) ///
> title("Great Britain", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") saving(uklinep, replace) yscale(range(-.1 0.3))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file uklinep.gph saved)

{com}. 
. *France
. logit PutinBi i.pr1##i.year female age agesq i.unedu [pweight=weight] if ccode ==220

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -2188.616}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2105.1503}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2099.5985}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2099.5937}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2099.5937}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,958
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    105.10
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2099.5937{txt}{col 49}Pseudo R2{col 67}= {res}    0.0407

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.0293495{col 31}{space 2}  .373846{col 42}{space 1}   -0.08{col 51}{space 3}0.937{col 59}{space 4}-.7620742{col 72}{space 3} .7033751
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2} .1425316{col 31}{space 2} .1833623{col 42}{space 1}    0.78{col 51}{space 3}0.437{col 59}{space 4}-.2168519{col 72}{space 3} .5019151
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .1388167{col 31}{space 2} .1648468{col 42}{space 1}    0.84{col 51}{space 3}0.400{col 59}{space 4} -.184277{col 72}{space 3} .4619104
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .5083943{col 31}{space 2} .1716198{col 42}{space 1}    2.96{col 51}{space 3}0.003{col 59}{space 4} .1720257{col 72}{space 3}  .844763
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .3972925{col 31}{space 2} .1741717{col 42}{space 1}    2.28{col 51}{space 3}0.023{col 59}{space 4} .0559222{col 72}{space 3} .7386627
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .6764846{col 31}{space 2} .4986251{col 42}{space 1}    1.36{col 51}{space 3}0.175{col 59}{space 4}-.3008026{col 72}{space 3} 1.653772
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .5871141{col 31}{space 2} .4625153{col 42}{space 1}    1.27{col 51}{space 3}0.204{col 59}{space 4}-.3193992{col 72}{space 3} 1.493627
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2}  .779892{col 31}{space 2} .4964782{col 42}{space 1}    1.57{col 51}{space 3}0.116{col 59}{space 4}-.1931874{col 72}{space 3} 1.752971
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} 1.235497{col 31}{space 2}  .490223{col 42}{space 1}    2.52{col 51}{space 3}0.012{col 59}{space 4} .2746777{col 72}{space 3} 2.196316
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.5239803{col 31}{space 2} .1042449{col 42}{space 1}   -5.03{col 51}{space 3}0.000{col 59}{space 4}-.7282965{col 72}{space 3} -.319664
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0642627{col 31}{space 2} .0139638{col 42}{space 1}   -4.60{col 51}{space 3}0.000{col 59}{space 4}-.0916312{col 72}{space 3}-.0368943
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0006504{col 31}{space 2} .0001377{col 42}{space 1}    4.72{col 51}{space 3}0.000{col 59}{space 4} .0003805{col 72}{space 3} .0009202
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.2760207{col 31}{space 2} .1040545{col 42}{space 1}   -2.65{col 51}{space 3}0.008{col 59}{space 4}-.4799637{col 72}{space 3}-.0720777
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.2948116{col 31}{space 2} .3536469{col 42}{space 1}   -0.83{col 51}{space 3}0.404{col 59}{space 4}-.9879468{col 72}{space 3} .3983236
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,958
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0030627{col 26}{space 2} .0386875{col 37}{space 1}   -0.08{col 46}{space 3}0.937{col 54}{space 4}-.0788888{col 67}{space 3} .0727634
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0935718{col 26}{space 2} .0551319{col 37}{space 1}    1.70{col 46}{space 3}0.090{col 54}{space 4}-.0144848{col 67}{space 3} .2016284
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .078268{col 26}{space 2} .0439706{col 37}{space 1}    1.78{col 46}{space 3}0.075{col 54}{space 4}-.0079127{col 67}{space 3} .1644487
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1357489{col 26}{space 2} .0672542{col 37}{space 1}    2.02{col 46}{space 3}0.044{col 54}{space 4} .0039331{col 67}{space 3} .2675647
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2282192{col 26}{space 2} .0703282{col 37}{space 1}    3.25{col 46}{space 3}0.001{col 54}{space 4} .0903785{col 67}{space 3}   .36606
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(black) lpattern(dash) lwidth(medthick) ) recastci(rline) /// 
> ciopts(fcolor(gs12)  lwidth(thick)) plotopt(msym(o) msize(vlarge)) ///
> title("France", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") saving(francelinep, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file francelinep.gph saved)

{com}. 
. *Germany
. logit PutinBi i.pr1##i.year female age agesq i.unedu  [pweight=weight] if ccode ==255

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2714.5774}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2614.0746}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2611.5978}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2611.5924}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2611.5924}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,901
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    131.46
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2611.5924{txt}{col 49}Pseudo R2{col 67}= {res}    0.0379

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2035259{col 31}{space 2} .4660313{col 42}{space 1}   -0.44{col 51}{space 3}0.662{col 59}{space 4} -1.11693{col 72}{space 3} .7098787
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2}-.0487821{col 31}{space 2} .1509102{col 42}{space 1}   -0.32{col 51}{space 3}0.747{col 59}{space 4}-.3445606{col 72}{space 3} .2469964
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.0616827{col 31}{space 2} .1499601{col 42}{space 1}   -0.41{col 51}{space 3}0.681{col 59}{space 4} -.355599{col 72}{space 3} .2322336
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .5006662{col 31}{space 2} .1414799{col 42}{space 1}    3.54{col 51}{space 3}0.000{col 59}{space 4} .2233708{col 72}{space 3} .7779616
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .1486553{col 31}{space 2} .1399202{col 42}{space 1}    1.06{col 51}{space 3}0.288{col 59}{space 4}-.1255833{col 72}{space 3} .4228938
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.398041{col 31}{space 2} .5690345{col 42}{space 1}    2.46{col 51}{space 3}0.014{col 59}{space 4} .2827543{col 72}{space 3} 2.513329
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.946307{col 31}{space 2} .5704026{col 42}{space 1}    3.41{col 51}{space 3}0.001{col 59}{space 4} .8283384{col 72}{space 3} 3.064276
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .5447612{col 31}{space 2} .5929528{col 42}{space 1}    0.92{col 51}{space 3}0.358{col 59}{space 4}-.6174049{col 72}{space 3} 1.706927
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .9647344{col 31}{space 2} .5751237{col 42}{space 1}    1.68{col 51}{space 3}0.093{col 59}{space 4}-.1624873{col 72}{space 3} 2.091956
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.6071267{col 31}{space 2} .0888649{col 42}{space 1}   -6.83{col 51}{space 3}0.000{col 59}{space 4}-.7812987{col 72}{space 3}-.4329546
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0317217{col 31}{space 2} .0131962{col 42}{space 1}   -2.40{col 51}{space 3}0.016{col 59}{space 4}-.0575857{col 72}{space 3}-.0058577
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0003388{col 31}{space 2} .0001269{col 42}{space 1}    2.67{col 51}{space 3}0.008{col 59}{space 4} .0000902{col 72}{space 3} .0005875
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0105445{col 31}{space 2} .0843772{col 42}{space 1}    0.12{col 51}{space 3}0.901{col 59}{space 4}-.1548317{col 72}{space 3} .1759207
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.4001647{col 31}{space 2} .3358894{col 42}{space 1}   -1.19{col 51}{space 3}0.234{col 59}{space 4}-1.058496{col 72}{space 3} .2581664
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,901
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0316185{col 26}{space 2}  .068529{col 37}{space 1}   -0.46{col 46}{space 3}0.645{col 54}{space 4} -.165933{col 67}{space 3} .1026959
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2482439{col 26}{space 2} .0764507{col 37}{space 1}    3.25{col 46}{space 3}0.001{col 54}{space 4} .0984032{col 67}{space 3} .3980845
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .3797609{col 26}{space 2}  .075829{col 37}{space 1}    5.01{col 46}{space 3}0.000{col 54}{space 4} .2311388{col 67}{space 3}  .528383
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0749466{col 26}{space 2} .0838453{col 37}{space 1}    0.89{col 46}{space 3}0.371{col 54}{space 4}-.0893872{col 67}{space 3} .2392804
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1587431{col 26}{space 2} .0772735{col 37}{space 1}    2.05{col 46}{space 3}0.040{col 54}{space 4} .0072898{col 67}{space 3} .3101965
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(black) lpattern(dash) lwidth(medthick) ) recastci(rline) /// 
> ciopts(fcolor(gs12)  lwidth(thick)) plotopt(msym(o) msize(vlarge)) ///
> title("Germany", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
> ytitle("") saving(germanylinep, replace) yscale(range(-.1 0.4))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file germanylinep.gph saved)

{com}. 
. *Italy
. logit PutinBi i.pr1##i.year female age agesq i.unedu [pweight=weight] if ccode ==325

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2525.3449}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2461.8583}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2460.6701}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2460.6699}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,620
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}     92.87
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2460.6699{txt}{col 49}Pseudo R2{col 67}= {res}    0.0256

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}          PutinBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.2560506{col 31}{space 2} .5650582{col 42}{space 1}   -0.45{col 51}{space 3}0.650{col 59}{space 4}-1.363544{col 72}{space 3} .8514431
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2014  {c |}{col 19}{res}{space 2} -.004822{col 31}{space 2} .1453871{col 42}{space 1}   -0.03{col 51}{space 3}0.974{col 59}{space 4}-.2897755{col 72}{space 3} .2801316
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .0232357{col 31}{space 2} .1403591{col 42}{space 1}    0.17{col 51}{space 3}0.869{col 59}{space 4} -.251863{col 72}{space 3} .2983344
{txt}{space 12}2016  {c |}{col 19}{res}{space 2} .7596363{col 31}{space 2} .1314068{col 42}{space 1}    5.78{col 51}{space 3}0.000{col 59}{space 4} .5020837{col 72}{space 3} 1.017189
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} .6093138{col 31}{space 2} .1426073{col 42}{space 1}    4.27{col 51}{space 3}0.000{col 59}{space 4} .3298086{col 72}{space 3}  .888819
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2014  {c |}{col 19}{res}{space 2} 1.263056{col 31}{space 2} .7010014{col 42}{space 1}    1.80{col 51}{space 3}0.072{col 59}{space 4}-.1108814{col 72}{space 3} 2.636994
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .7597702{col 31}{space 2} .6680788{col 42}{space 1}    1.14{col 51}{space 3}0.255{col 59}{space 4}-.5496402{col 72}{space 3} 2.069181
{txt}{space 8}AEP#2016  {c |}{col 19}{res}{space 2} .9484244{col 31}{space 2} .6410299{col 42}{space 1}    1.48{col 51}{space 3}0.139{col 59}{space 4}-.3079711{col 72}{space 3}  2.20482
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .2650134{col 31}{space 2} .6825796{col 42}{space 1}    0.39{col 51}{space 3}0.698{col 59}{space 4}-1.072818{col 72}{space 3} 1.602845
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.2048076{col 31}{space 2} .0828202{col 42}{space 1}   -2.47{col 51}{space 3}0.013{col 59}{space 4}-.3671321{col 72}{space 3}-.0424831
{txt}{space 14}age {c |}{col 19}{res}{space 2} .0065411{col 31}{space 2} .0139501{col 42}{space 1}    0.47{col 51}{space 3}0.639{col 59}{space 4}-.0208006{col 72}{space 3} .0338827
{txt}{space 12}agesq {c |}{col 19}{res}{space 2}-.0000751{col 31}{space 2} .0001399{col 42}{space 1}   -0.54{col 51}{space 3}0.592{col 59}{space 4}-.0003492{col 72}{space 3} .0001991
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .0498926{col 31}{space 2} .0941163{col 42}{space 1}    0.53{col 51}{space 3}0.596{col 59}{space 4}-.1345719{col 72}{space 3} .2343571
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-1.494366{col 31}{space 2} .3422359{col 42}{space 1}   -4.37{col 51}{space 3}0.000{col 59}{space 4}-2.165136{col 72}{space 3} -.823596
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2014 2015 2016 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,620
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(PutinBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2016}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0359724{col 26}{space 2} .0732059{col 37}{space 1}   -0.49{col 46}{space 3}0.623{col 54}{space 4}-.1794533{col 67}{space 3} .1075084
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1989959{col 26}{space 2} .0964843{col 37}{space 1}    2.06{col 46}{space 3}0.039{col 54}{space 4} .0098901{col 67}{space 3} .3881017
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0899225{col 26}{space 2} .0707005{col 37}{space 1}    1.27{col 46}{space 3}0.203{col 54}{space 4} -.048648{col 67}{space 3} .2284929
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1656521{col 26}{space 2}  .074406{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0198191{col 67}{space 3} .3114851
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0018754{col 26}{space 2} .0799869{col 37}{space 1}    0.02{col 46}{space 3}0.981{col 54}{space 4} -.154896{col 67}{space 3} .1586468
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(black) lpattern(dash) lwidth(medthick) ) recastci(rline) /// 
> ciopts(fcolor(gs12)  lwidth(thick)) plotopt(msym(o) msize(vlarge)) ///
> title("Italy", margin(0 0 5 0) size(medium)  position(12) span)  scheme(538bw) ///
> ytitle("") saving(italylinep, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file italylinep.gph saved)

{com}.   
. gr combine uklinep.gph francelinep.gph germanylinep.gph italylinep.gph, ycommon iscale(0.8) /// 
> l1("Confidence in Vladimir Putin: Difference"  "Between AEP and non-AEP Supporters", size(small)) ///
> note("{c -(}it:Note:{c )-} Models with probability weights. AEP refers to Die Linke voters in Germany, National Front voters in France, UKIP voters in Great Britain, and Lega Nord voters in Italy."  "Germany (N=4,901), France (N=4,958), Great Britain (4,944), and Italy (4,620). Estimated with logistic regression with demographic controls, AMEs with 95% CIs.", size(tiny) span)  saving(weightsgraphs.gph, replace)
{res}{txt}(note: file weightsgraphs.gph not found)
{res}{txt}(file weightsgraphs.gph saved)

{com}. graph export "putin_dydx_weight", as(eps) replace
{txt}(note: file putin_dydx_weight not found)
(file putin_dydx_weight written in EPS format)

{com}. 
. ****************************
. *Favorability toward Russia*
. ****************************
. 
. use "/Users/Alex2/Desktop/Documents/GW Fall 2016/Russia Public Opinion Paper/TNPN/TrickleDown_DataSet_Oct2018.dta"
{err}no; data in memory would be lost
{txt}{search r(4), local:r(4);}

end of do-file

{search r(4), local:r(4);}

{com}. do "/var/folders/yd/86_pg0qs3wzf4_z28x2_2m9h0000gr/T//SD00300.000000"
{txt}
{com}. *United Kingdom
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu [pweight=weight] if ccode ==200

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -2648.653}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2493.4804}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2491.9133}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2491.9116}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2491.9116}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,080
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    218.82
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2491.9116{txt}{col 49}Pseudo R2{col 67}= {res}    0.0592

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.1520023{col 31}{space 2} .3243238{col 42}{space 1}   -0.47{col 51}{space 3}0.639{col 59}{space 4}-.7876653{col 72}{space 3} .4836607
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .0720157{col 31}{space 2} .1157195{col 42}{space 1}    0.62{col 51}{space 3}0.534{col 59}{space 4}-.1547903{col 72}{space 3} .2988217
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.8439501{col 31}{space 2} .1208234{col 42}{space 1}   -6.98{col 51}{space 3}0.000{col 59}{space 4} -1.08076{col 72}{space 3}-.6071405
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-1.333764{col 31}{space 2} .1411185{col 42}{space 1}   -9.45{col 51}{space 3}0.000{col 59}{space 4}-1.610352{col 72}{space 3}-1.057177
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.7560948{col 31}{space 2} .1241705{col 42}{space 1}   -6.09{col 51}{space 3}0.000{col 59}{space 4}-.9994645{col 72}{space 3} -.512725
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .2845165{col 31}{space 2} .4086276{col 42}{space 1}    0.70{col 51}{space 3}0.486{col 59}{space 4}-.5163788{col 72}{space 3} 1.085412
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .3092489{col 31}{space 2} .4257582{col 42}{space 1}    0.73{col 51}{space 3}0.468{col 59}{space 4}-.5252219{col 72}{space 3}  1.14372
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .7873953{col 31}{space 2} .4818168{col 42}{space 1}    1.63{col 51}{space 3}0.102{col 59}{space 4}-.1569483{col 72}{space 3} 1.731739
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}  .514513{col 31}{space 2} .5846199{col 42}{space 1}    0.88{col 51}{space 3}0.379{col 59}{space 4}-.6313211{col 72}{space 3} 1.660347
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}  -.26845{col 31}{space 2} .0781943{col 42}{space 1}   -3.43{col 51}{space 3}0.001{col 59}{space 4}-.4217079{col 72}{space 3}-.1151921
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0596087{col 31}{space 2} .0114216{col 42}{space 1}   -5.22{col 51}{space 3}0.000{col 59}{space 4}-.0819946{col 72}{space 3}-.0372227
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0004684{col 31}{space 2} .0001093{col 42}{space 1}    4.29{col 51}{space 3}0.000{col 59}{space 4} .0002543{col 72}{space 3} .0006825
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.0782019{col 31}{space 2} .0859158{col 42}{space 1}   -0.91{col 51}{space 3}0.363{col 59}{space 4}-.2465938{col 72}{space 3} .0901899
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.690453{col 31}{space 2} .2956531{col 42}{space 1}    5.72{col 51}{space 3}0.000{col 59}{space 4} 1.110984{col 72}{space 3} 2.269923
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1) at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,080
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0367373{col 26}{space 2} .0778563{col 37}{space 1}   -0.47{col 46}{space 3}0.637{col 54}{space 4}-.1893328{col 67}{space 3} .1158582
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0323039{col 26}{space 2} .0607097{col 37}{space 1}    0.53{col 46}{space 3}0.595{col 54}{space 4}-.0866849{col 67}{space 3} .1512927
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0321676{col 26}{space 2} .0577965{col 37}{space 1}    0.56{col 46}{space 3}0.578{col 54}{space 4}-.0811115{col 67}{space 3} .1454467
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1163153{col 26}{space 2} .0731504{col 37}{space 1}    1.59{col 46}{space 3}0.112{col 54}{space 4}-.0270569{col 67}{space 3} .2596876
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0791375{col 26}{space 2} .1114646{col 37}{space 1}    0.71{col 46}{space 3}0.478{col 54}{space 4} -.139329{col 67}{space 3} .2976041
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(black) lpattern(dash) lwidth(medthick) ) recastci(rline) /// 
> ciopts(fcolor(gs12)  lwidth(thick)) plotopt(msym(o) msize(vlarge)) ///
> title("Great Britain", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
>  ytitle("") saving(ukline, replace) yscale(range(-.1 0.3))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file ukline.gph saved)

{com}.  
. *France
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu [pweight=weight] if ccode ==220

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-3161.2823}  
Iteration 1:{space 3}log pseudolikelihood = {res:-3048.1759}  
Iteration 2:{space 3}log pseudolikelihood = {res:-3047.4946}  
Iteration 3:{space 3}log pseudolikelihood = {res:-3047.4945}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,978
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    143.49
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-3047.4945{txt}{col 49}Pseudo R2{col 67}= {res}    0.0360

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .2157354{col 31}{space 2} .3080506{col 42}{space 1}    0.70{col 51}{space 3}0.484{col 59}{space 4}-.3880326{col 72}{space 3} .8195035
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}-.0438954{col 31}{space 2} .1171982{col 42}{space 1}   -0.37{col 51}{space 3}0.708{col 59}{space 4}-.2735996{col 72}{space 3} .1858089
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.5503122{col 31}{space 2} .1313666{col 42}{space 1}   -4.19{col 51}{space 3}0.000{col 59}{space 4}-.8077859{col 72}{space 3}-.2928385
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} -.357492{col 31}{space 2} .1165703{col 42}{space 1}   -3.07{col 51}{space 3}0.002{col 59}{space 4}-.5859656{col 72}{space 3}-.1290184
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}-.0233196{col 31}{space 2} .1273317{col 42}{space 1}   -0.18{col 51}{space 3}0.855{col 59}{space 4}-.2728852{col 72}{space 3}  .226246
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2}-.0505971{col 31}{space 2} .3991766{col 42}{space 1}   -0.13{col 51}{space 3}0.899{col 59}{space 4}-.8329688{col 72}{space 3} .7317747
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2}  .094983{col 31}{space 2} .4273163{col 42}{space 1}    0.22{col 51}{space 3}0.824{col 59}{space 4}-.7425415{col 72}{space 3} .9325075
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .4334019{col 31}{space 2} .3889005{col 42}{space 1}    1.11{col 51}{space 3}0.265{col 59}{space 4} -.328829{col 72}{space 3} 1.195633
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2}   .79494{col 31}{space 2} .4642121{col 42}{space 1}    1.71{col 51}{space 3}0.087{col 59}{space 4} -.114899{col 72}{space 3} 1.704779
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.3504782{col 31}{space 2} .0764605{col 42}{space 1}   -4.58{col 51}{space 3}0.000{col 59}{space 4} -.500338{col 72}{space 3}-.2006185
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0670114{col 31}{space 2} .0107762{col 42}{space 1}   -6.22{col 51}{space 3}0.000{col 59}{space 4}-.0881323{col 72}{space 3}-.0458905
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0005508{col 31}{space 2} .0001069{col 42}{space 1}    5.15{col 51}{space 3}0.000{col 59}{space 4} .0003413{col 72}{space 3} .0007602
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.4029872{col 31}{space 2} .0778583{col 42}{space 1}   -5.18{col 51}{space 3}0.000{col 59}{space 4}-.5555867{col 72}{space 3}-.2503877
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.504815{col 31}{space 2} .2660924{col 42}{space 1}    5.66{col 51}{space 3}0.000{col 59}{space 4} .9832831{col 72}{space 3} 2.026346
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1)  at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,978
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0494654{col 26}{space 2} .0719466{col 37}{space 1}    0.69{col 46}{space 3}0.492{col 54}{space 4}-.0915473{col 67}{space 3} .1904781
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  .037277{col 26}{space 2} .0586114{col 37}{space 1}    0.64{col 46}{space 3}0.525{col 54}{space 4}-.0775991{col 67}{space 3} .1521532
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0606488{col 26}{space 2} .0609865{col 37}{space 1}    0.99{col 46}{space 3}0.320{col 54}{space 4}-.0588825{col 67}{space 3} .1801801
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1430215{col 26}{space 2} .0557192{col 37}{space 1}    2.57{col 46}{space 3}0.010{col 54}{space 4} .0338139{col 67}{space 3} .2522292
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2392026{col 26}{space 2} .0808759{col 37}{space 1}    2.96{col 46}{space 3}0.003{col 54}{space 4} .0806888{col 67}{space 3} .3977165
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(black) lpattern(dash) lwidth(medthick) ) recastci(rline) /// 
> ciopts(fcolor(gs12)  lwidth(thick)) plotopt(msym(o) msize(vlarge)) ///
> title("France", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
>  ytitle("") saving(franceline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file franceline.gph saved)

{com}. 
. *Germany
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu [pweight=weight] if ccode ==255

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2842.7976}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2678.0251}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2675.1882}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2675.1845}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2675.1845}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,802
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    186.07
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2675.1845{txt}{col 49}Pseudo R2{col 67}= {res}    0.0590

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2} .1331277{col 31}{space 2} .5010133{col 42}{space 1}    0.27{col 51}{space 3}0.790{col 59}{space 4}-.8488404{col 72}{space 3} 1.115096
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2}   .02641{col 31}{space 2} .1327536{col 42}{space 1}    0.20{col 51}{space 3}0.842{col 59}{space 4}-.2337824{col 72}{space 3} .2866023
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.8484942{col 31}{space 2} .1495307{col 42}{space 1}   -5.67{col 51}{space 3}0.000{col 59}{space 4}-1.141569{col 72}{space 3}-.5554193
{txt}{space 12}2015  {c |}{col 19}{res}{space 2}-.4328327{col 31}{space 2} .1371511{col 42}{space 1}   -3.16{col 51}{space 3}0.002{col 59}{space 4} -.701644{col 72}{space 3}-.1640214
{txt}{space 12}2017  {c |}{col 19}{res}{space 2} -.262849{col 31}{space 2} .1350107{col 42}{space 1}   -1.95{col 51}{space 3}0.052{col 59}{space 4} -.527465{col 72}{space 3} .0017671
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .0743804{col 31}{space 2} .6399565{col 42}{space 1}    0.12{col 51}{space 3}0.907{col 59}{space 4}-1.179911{col 72}{space 3} 1.328672
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .6977711{col 31}{space 2} .6293565{col 42}{space 1}    1.11{col 51}{space 3}0.268{col 59}{space 4} -.535745{col 72}{space 3} 1.931287
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} 1.546117{col 31}{space 2} .5948492{col 42}{space 1}    2.60{col 51}{space 3}0.009{col 59}{space 4} .3802338{col 72}{space 3}    2.712
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .0373445{col 31}{space 2} .6019864{col 42}{space 1}    0.06{col 51}{space 3}0.951{col 59}{space 4}-1.142527{col 72}{space 3} 1.217216
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.7891204{col 31}{space 2} .0876851{col 42}{space 1}   -9.00{col 51}{space 3}0.000{col 59}{space 4}  -.96098{col 72}{space 3}-.6172608
{txt}{space 14}age {c |}{col 19}{res}{space 2} -.070099{col 31}{space 2} .0128899{col 42}{space 1}   -5.44{col 51}{space 3}0.000{col 59}{space 4}-.0953626{col 72}{space 3}-.0448353
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0006639{col 31}{space 2} .0001258{col 42}{space 1}    5.28{col 51}{space 3}0.000{col 59}{space 4} .0004173{col 72}{space 3} .0009105
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2}-.1556252{col 31}{space 2} .0824215{col 42}{space 1}   -1.89{col 51}{space 3}0.059{col 59}{space 4}-.3171684{col 72}{space 3} .0059179
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 1.364079{col 31}{space 2} .3242018{col 42}{space 1}    4.21{col 51}{space 3}0.000{col 59}{space 4} .7286548{col 72}{space 3} 1.999502
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1)  at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,802
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  .028825{col 26}{space 2} .1102686{col 37}{space 1}    0.26{col 46}{space 3}0.794{col 54}{space 4}-.1872975{col 67}{space 3} .2449474
{txt}{space 10}2  {c |}{col 14}{res}{space 2}   .04566{col 26}{space 2} .0896825{col 37}{space 1}    0.51{col 46}{space 3}0.611{col 54}{space 4}-.1301144{col 67}{space 3} .2214344
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1491595{col 26}{space 2} .0785968{col 37}{space 1}    1.90{col 46}{space 3}0.058{col 54}{space 4}-.0048875{col 67}{space 3} .3032065
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3722375{col 26}{space 2} .0705958{col 37}{space 1}    5.27{col 46}{space 3}0.000{col 54}{space 4} .2338723{col 67}{space 3} .5106027
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0341369{col 26}{space 2} .0685183{col 37}{space 1}    0.50{col 46}{space 3}0.618{col 54}{space 4}-.1001566{col 67}{space 3} .1684303
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(black) lpattern(dash) lwidth(medthick) ) recastci(rline) /// 
> ciopts(fcolor(gs12)  lwidth(thick)) plotopt(msym(o) msize(vlarge)) ///
> title("Germany", margin(0 0 5 0) size(medium)  position(12) span) scheme(538bw) ///
>  ytitle("") saving(germanyline, replace) yscale(range(-.1 0.4))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file germanyline.gph saved)

{com}. 
. *Italy
. logit SupportRussiaBi i.pr1##i.year female age agesq i.unedu [pweight=weight] if ccode ==325

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2807.7155}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2746.7423}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2746.3385}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2746.3384}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     4,681
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}     86.11
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2746.3384{txt}{col 49}Pseudo R2{col 67}= {res}    0.0219

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}  SupportRussiaBi{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}pr1 {c |}
{space 13}AEP  {c |}{col 19}{res}{space 2}-.4429275{col 31}{space 2} .6923147{col 42}{space 1}   -0.64{col 51}{space 3}0.522{col 59}{space 4}-1.799839{col 72}{space 3} .9139843
{txt}{space 17} {c |}
{space 13}year {c |}
{space 12}2013  {c |}{col 19}{res}{space 2} .4987904{col 31}{space 2}  .113432{col 42}{space 1}    4.40{col 51}{space 3}0.000{col 59}{space 4} .2764679{col 72}{space 3}  .721113
{txt}{space 12}2014  {c |}{col 19}{res}{space 2}-.1890736{col 31}{space 2} .1320007{col 42}{space 1}   -1.43{col 51}{space 3}0.152{col 59}{space 4}-.4477903{col 72}{space 3}  .069643
{txt}{space 12}2015  {c |}{col 19}{res}{space 2} .1269123{col 31}{space 2} .1237331{col 42}{space 1}    1.03{col 51}{space 3}0.305{col 59}{space 4}-.1156001{col 72}{space 3} .3694247
{txt}{space 12}2017  {c |}{col 19}{res}{space 2}  .670298{col 31}{space 2} .1295297{col 42}{space 1}    5.17{col 51}{space 3}0.000{col 59}{space 4} .4164243{col 72}{space 3} .9241716
{txt}{space 17} {c |}
{space 9}pr1#year {c |}
{space 8}AEP#2013  {c |}{col 19}{res}{space 2} .5601599{col 31}{space 2} .8430456{col 42}{space 1}    0.66{col 51}{space 3}0.506{col 59}{space 4}-1.092179{col 72}{space 3} 2.212499
{txt}{space 8}AEP#2014  {c |}{col 19}{res}{space 2} .6007347{col 31}{space 2} .8052756{col 42}{space 1}    0.75{col 51}{space 3}0.456{col 59}{space 4}-.9775764{col 72}{space 3} 2.179046
{txt}{space 8}AEP#2015  {c |}{col 19}{res}{space 2} .8083603{col 31}{space 2} .7658858{col 42}{space 1}    1.06{col 51}{space 3}0.291{col 59}{space 4}-.6927483{col 72}{space 3} 2.309469
{txt}{space 8}AEP#2017  {c |}{col 19}{res}{space 2} .3997074{col 31}{space 2} .7793692{col 42}{space 1}    0.51{col 51}{space 3}0.608{col 59}{space 4}-1.127828{col 72}{space 3} 1.927243
{txt}{space 17} {c |}
{space 11}female {c |}{col 19}{res}{space 2}-.0922824{col 31}{space 2}   .07428{col 42}{space 1}   -1.24{col 51}{space 3}0.214{col 59}{space 4}-.2378685{col 72}{space 3} .0533036
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0201901{col 31}{space 2} .0128107{col 42}{space 1}   -1.58{col 51}{space 3}0.115{col 59}{space 4}-.0452987{col 72}{space 3} .0049184
{txt}{space 12}agesq {c |}{col 19}{res}{space 2} .0001267{col 31}{space 2} .0001313{col 42}{space 1}    0.96{col 51}{space 3}0.335{col 59}{space 4}-.0001307{col 72}{space 3} .0003842
{txt}{space 17} {c |}
{space 12}unedu {c |}
Higher Education  {c |}{col 19}{res}{space 2} .1047849{col 31}{space 2} .0819851{col 42}{space 1}    1.28{col 51}{space 3}0.201{col 59}{space 4}-.0559029{col 72}{space 3} .2654728
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-.4426446{col 31}{space 2} .3064472{col 42}{space 1}   -1.44{col 51}{space 3}0.149{col 59}{space 4} -1.04327{col 72}{space 3}  .157981
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(pr1)  at(year=(2012 2013 2014 2015 2017))
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,681
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(SupportRussiaBi), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.pr1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2012}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2013}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2014}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2015}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:year}{space 12}{txt:=} {space 7}2017}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.pr1        {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0733807{col 26}{space 2} .1007781{col 37}{space 1}   -0.73{col 46}{space 3}0.467{col 54}{space 4}-.2709021{col 67}{space 3} .1241407
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0270566{col 26}{space 2} .1130342{col 37}{space 1}    0.24{col 46}{space 3}0.811{col 54}{space 4}-.1944864{col 67}{space 3} .2485996
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0278057{col 26}{space 2} .0750271{col 37}{space 1}    0.37{col 46}{space 3}0.711{col 54}{space 4}-.1192447{col 67}{space 3} .1748561
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0778466{col 26}{space 2} .0742297{col 37}{space 1}    1.05{col 46}{space 3}0.294{col 54}{space 4} -.067641{col 67}{space 3} .2233342
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0101993{col 26}{space 2} .0845255{col 37}{space 1}   -0.12{col 46}{space 3}0.904{col 54}{space 4}-.1758662{col 67}{space 3} .1554676
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. marginsplot, yline(0, lcolor(black) lpattern(dash) lwidth(medthick) ) recastci(rline) /// 
> ciopts(fcolor(gs12)  lwidth(thick)) plotopt(msym(o) msize(vlarge)) ///
> title("Italy", margin(0 0 5 0) size(medium)  position(12) span)  scheme(538bw) ///
>  ytitle("") saving(italyline, replace) yscale(range(-.1 0.2))

{text}{p 2 6 2}Variables that uniquely identify margins: year{p_end}
{res}{txt}(file italyline.gph saved)

{com}.  
. *With NO IDEOLOGY CONTROL 
. gr combine ukline.gph franceline.gph germanyline.gph italyline.gph, ycommon iscale(0.8) /// 
> l1("Favorability toward Russia: Difference" "Between AEP and non-AEP Supporters", size(small)) ///
> note("{c -(}it:Note:{c )-} Models with probability weights. AEP refers to Die Linke voters in Germany, National Front voters in France, UKIP voters in Great Britain, and Lega Nord voters in Italy." "Germany (N=4,802), France (N=4,978), Great Britain (N=4,080), and Italy (4,681). Estimated with logistic regression with demographic controls, AMEs with 95% CIs.", size(tiny) span) 
{res}{txt}
{com}. graph export "russia_dydx_weight", as(eps) replace
{txt}(note: file russia_dydx_weight not found)
(file russia_dydx_weight written in EPS format)

{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/Alex2/Desktop/Documents/GW Fall 2016/Russia Public Opinion Paper/TNPN/Fisher_FPA_TrickleDown_Log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}24 Sep 2019, 21:36:22
{txt}{.-}
{smcl}
{txt}{sf}{ul off}